Showing posts with label risk-trust-security. Show all posts
Showing posts with label risk-trust-security. Show all posts

Thursday, November 07, 2019

On Magic Numbers - Privacy and Security

People and organizations often adopt a metrical approach to sensemaking, decision and policy. They attach numbers to things, perhaps using a weighted scorecard or other calculation method, and then make judgements about status or priority or action based on these numbers. Sometimes called triage.

In the simplest version, a single number is produced. More complex versions may involve producing several numbers (sometimes called a vector). For example, if an item can be represented by a pair of numbers, these can be used to position the item on a 2x2 quadrant. See my post Into The Matrix.

In this post, I shall look at how this approach works for managing risk, security and privacy.



A typical example of security scoring is the Common Vulnerability Scoring System (CVSS), which assigns numbers to security vulnerabilities. These numbers may determine or influence the allocation of resources within the security field.

Scoring systems are sometimes used within the privacy field as part of Privacy by Design (PbD) or Data Protection Impact Assessment (DPIA). The resultant numbers are used to decide whether something is acceptable, unacceptable or borderline. And in 2013, two researchers at ENISA published a scoring system for assessing the severity of data breaches. Scores less than 2 indicated low severity, scores higher than 4 indicated very high severity.

The advantage of these systems is that they are (relatively) quick and repeatable, especially across large diverse organizations with variable levels of subject matter expertise. The results are typically regarded as objective, and may therefore be taken more seriously by senior management and other stakeholders.

However, these systems are merely indicative, and the scores may not always provide a reliable or accurate view. For example, I doubt whether any Data Protection Officer would be justified in disregarding a potential data breach simply on the basis of a low score from an uncalibrated calculation.

Part of the problem is that these scoring systems operate a highly simplistic algebra, assuming you can break a complex situation into an number of separate factors (e.g. vulnerabilities), and then add them back together with some appropriate weightings. The weightings can be pretty arbitrary, and may not be valid for your organization. More importantly, as Marc Rogers argues (as reported by Shaun Nichols), the more sophisticated attacks rely on combinations of vulnerabilities, so assessing each vulnerability separately completely misses the point.

Thus although two minor bugs may have low CVSS ratings, interaction between them could allow a high severity attack. It is complex, but there is nothing in the assessment process to deal with that, Rogers said. It has lulled us into a false sense of security where we look at the score, and so long as it is low we don't allocate the resources.

One organization that has moved away from the scorecard approach is the Electronic Frontier Foundation. In 2014, they released a Secure Messaging Scorecard for evaluating messaging apps. However, they later decided that the scorecard format dangerously oversimplified the complex question of how various messengers stack up from a security perspective, so they archived the original scorecard and warned people against relying on it.




Nate Cardozo, Gennie Gebhart and Erica Portnoy, Secure Messaging? More Like A Secure Mess (Electronic Frontier Foundation, 26 March 2018)

Clara Galan Manso and Sławomir Górniak, Recommendations for a methodology of the assessment of severity of personal data breaches (ENISA 2013)

Shaun Nichols, We're almost into the third decade of the 21st century and we're still grading security bugs out of 10 like kids. Why? (The Register, 7 Nov 2019)

Wikipedia: Common Vulnerability Scoring System (CVSS)

Related posts: Into The Matrix (October 2015), False Sense of Security (June 2019)

Tuesday, July 24, 2018

Evidence-Based Planning

Everybody's favourite internet-book-retailer-cum-cloud-computing-giant is planning for a wide range of outcomes after Brexit.
"Like any business, we consider a wide range of scenarios in planning discussions so that we’re prepared to continue serving customers and small businesses who count on Amazon, even if those scenarios are very unlikely," a spokesperson said.

However, a Government spokesperson dismissed speculation about civil unrest, saying
"Where is the evidence to suggest that would happen?"

To which one might counter

"Where is the evidence to suggest that wouldn't happen?"



There is a methodological gulf between these two positions. One is planning for things you can't prove won't happen. The other is NOT planning for things you can't prove WILL happen.

The political problem with planning for things that might not happen, is that people may criticize you for wasting time and money on something that didn't happen. Whereas if you fail to plan for something that is unlikely to happen, and then it does happen, you can appeal to bad luck. Or the wrong kind of snow.

As with other modes of decision-making, planning simply to avoid censure is not necessarily conducive to good outcomes.


Gareth Corfield, I predict a riot: Amazon UK chief foresees 'civil unrest' for no-deal Brexit (The Register, 23 July 2018)

Rob Davies, No-deal Brexit risks 'civil unrest', warns Amazon's UK boss (The Guardian, 23 July 2018)

Related Post: Decision-Making Models (March 2017)

Monday, June 04, 2018

Outdated Assumptions - Connectivity Hunger

Behaviours developed in a state of scarcity may cease to be appropriate in a state of abundance. Our stone age ancestors struggled to get enough energy-rich food, so they acquired a taste for food with a strong energy hit. We inherited a greed for sweet and fatty foods, and can now stuff our faces on delicacies our stone age ancestors never knew, such as ice-cream and cheesecake.

***

So let's talk about data. Once upon a time, data processing systems struggled to get enough data, and long-term data storage was expensive, so we were told to regard data as an asset. People learned to grab as much data as they could, and keep it until the data storage was full. But the greed for data was always moderated by the cost of collection, storage and retrieval, as well as the limited choice of data that was available in the first place.

Take away the assumption of data scarcity and cost, and our greed for data becomes problematic. We now recognize that data (especially personal data) can be a liability as much as an asset, and have become wedded to the principle of data minimization - only collecting the data you need, and only keeping it as long as you need.

***

But data scarcity is not the only outdated assumption that still influences our behaviour. Let's also talk about connectivity. Once upon a time, connectivity was intermittent, slow, unreliable. Hungry for greater connectivity, computer scientists dreamed of a world where everything was always on. More recently, Facebook has argued that Connectivity is a Human Right. (But you can only read this document if you have a Facebook account!)

But as with an overabundance of data, we may experience an overabundance of connectivity. Thus we are starting to realise the downside of the "always on", not just in the highly insecure world of the Internet of Things (Rainie and Anderson) but also in corporate computing (Ben-Meir, Hill).

Increasingly, products and services are being designed for "always on" operation. Ben-Meir notes Apple’s assertion that constant connectivity is essential for features such as AirDrop and AirPlay, and only today a colleague was grumbling to me about the downgrading of offline functionality in Microsoft Outlook.

Perhaps therefore, similar to the data minimization principle, there needs to be a network minimization principle. The wider the network, the larger the scope of responsibility. Or as Bruce Schneier puts it, "the more we network things together, the more vulnerabilities on one thing will affect other things". So don’t just connect because you can. Connect for a reason, disconnect by default, support offline functionality and disruption-tolerance, prefer secure hubs to insecure peer-to-peer.

Bruce Schneier again: "We also need to reverse the trend to connect everything to the internet. And if we risk harm and even death, we need to think twice about what we connect and what we deliberately leave uncomputerized. If we get this wrong, the computer industry will look like the pharmaceutical industry, or the aircraft industry. But if we get this right, we can maintain the innovative environment of the internet that has given us so much."



Elad Ben-Meir, How an 'Always-On' Culture Compromises Corporate Security (Info Security, 2 November 2017)

Paul Hill, Always-on Access Brings Always-Threatening Security Risks (System Experts, 25 June 2015)

Lee Rainie and Janna Anderson, The Internet of Things Connectivity Binge: What Are the Implications? (Pew Research Centre, 6 June 2017)

Bruce Schneier, Click Here to Kill Everyone (New York Magazine, 27 January 2017)

Maeve Shearlaw, Mark Zuckerberg says connectivity is a basic human right – do you agree? (Guardian 3 Jan 2014)

Related post: Pax Technica - On Risk and Security (November 2017)


Thanks to @futureidentity for useful discussion

Saturday, December 02, 2017

UK Retail Data Breaches

Some people talk as if data protection and security must be fixed before May 2018 because of GDPR. Wrong. Data protection and security must be fixed now.

Morrisons (2014)


The High Court has just found Morrisons to be liable for a leak of employee data by a disaffected employee in 2014. (The perpetrator got eight years in jail.) 

http://www.theregister.co.uk/2017/12/01/morrisons_data_leak_ruling/
http://www.bbc.co.uk/news/uk-england-42193502

Sports Direct (2016)


A hacker obtained employee details in September 2016, but Sports Direct failed to communicate the breach to the affected employees.

https://www.theregister.co.uk/2017/02/08/sports_direct_fails_to_inform_staff_over_hack_and_data_breach/

CEX (2017)


Second-hand gadget and video games retailer Cex has said up to two million customers have had their data stolen in an online breach

http://www.bbc.co.uk/news/technology-41095162
https://uk.webuy.com/guidance/

Zomato (2017)


Up to 17 million users affected by data breach at restaurant search platform Zomato

https://www.infosecurity-magazine.com/news/zomato-breach-exposes-17-million/
https://www.zomato.com/blog/security-notice

Tesco Bank (2016)


Cyber thieves steal £2.5m

https://www.theguardian.com/business/2016/nov/08/tesco-bank-cyber-thieves-25m
https://www.theregister.co.uk/2016/11/10/tesco_bank_breach_analysis/
https://www.itproportal.com/features/lessons-from-the-tesco-bank-hack/



Related posts


The Smell of Data (December 2017)

Tuesday, June 27, 2017

Digital Disruption and Consumer Trust - Resolving the Challenge of GDPR

Presentation given to the "GDPR Making it Real" workshop organized by DAMA UK and BCS DMSG, 12 June 2017.

The presentation refers to two milestones. The second milestone is 25th May 2018, the date that companies will need to comply fully with the new data protection regulations. The first milestone is the agreement of a clear and costed plan to reach the second milestone. Some organizations are now getting close to the first milestone, while others still don't have much idea how much effort and resource will be required, or how this could affect their business. Good luck with that. Let me know if I can help.


Friday, May 05, 2017

The Price of Everything

The relationship between the retailer and the customer can be beset by calculation on both sides. The retailer is trying to extract enough data about the customer to calculate the next best action, while the customer is trying to extract the best deal.

There is nothing new about customers comparing products and prices between neighbouring shops, and merchants selling similar goods can often be found in close proximity in order to attract more customers. (This is especially true for specialist and occasional purchases: in large cities, whole streets or districts may be associated with specific types of shop. London has Denmark Street for musical instruments, Hatton Garden for jewellery, Saville Row for made-to-measure suits, and so on.)

And as Tim Harford points out, exploitative algorithms are using tricks as old as haggling at the bazaar.

But nowadays the villain, apparently, is eCommerce. As a significant share of the retail business migrates from the high street to the Internet, many retailers are concerned about so-called showrooming. It may seem unfair that a customer can spend loads of time in the high street, wasting the time of the shop assistants and shop-soiling the goods, before purchasing the same goods online at a better price. To add insult to injury, some people not only practice showrooming, but then blog about how guilty it makes them feel.

There is a common belief that the Internet can generally undercut the High Street, and there are several reasons why this belief seems to make sense.
  • Internet businesses compete on price rather than service, so the prices must be good.
  • An internet store can provide economies of scale - serving the whole country or region from a single warehouse, instead of needing an outlet in each town.
  • An internet store can offer a much larger range of goods without increasing the cost of inventory - the so-called Long Tail phenomenon
  • An internet store typically has lower overheads - cheaper premises and fewer staff
  • An internet business may be run as a start-up, with less dead wood. So it is more agile and less bureaucratic. 
But there are some questionable assumptions here, as well as some counterbalancing concerns.
  • The economic and logistical costs of delivery and return can be significant, especially for low-ticket items. With clothing in particular, customers may order the same item in three different sizes, and then return the ones that don't fit.
  • Investors previously poured money into internet businesses, and the early strategic focus was on growth rather than profit. As internet business become more mature, investors will be looking to see some decent returns on their investment, and margins will be pushed up.
  • And then there is differential pricing ...
One of the key differences between traditional stores and online stores is in pricing. Although high street retailers often drop prices to clear stock - for example, supermarkets have elaborate relabelling systems to mark-down groceries before their sell-by date - they do not yet have sophisticated mechanisms for dynamic pricing. Whereas an online retailer can change the prices as often as it wishes, and therefore charge you whatever it thinks you will pay. According to Jerry Useem,
The price of the headphones Google recommends may depend on how budget-conscious your web history shows you to be.
I heard Ariel Ezrachi talking about this phenomenon at the PowerSwitch conference in Cambridge a few weeks ago. (I have not yet read his new book.)
There is an assumption is that the internet is a blessing when it comes to competition. Endless choice. Ability to reduce costs to close to zero. etc ... What you see online has very little to do with the ideas we have of market power, market dynamics, etc. everything is artificial. It looks like a regular market, with apples or fish. But because it’s all monitored, it’s not like that at all. What you see online is not a reflection of the market. You see the Truman Show — a reality designed just for you, a controlled ecosystem. (via Laura James's liveblog)

In his play Lady Windermere's Fan, Wilde offered the following contrast between the cynic and the sentimentalist.
Lord Darlington: What cynics you fellows are!
Cecil Graham: What is a cynic?
Lord Darlington: A man who knows the price of everything and the value of nothing.
Cecil Graham: And a sentimentalist, my dear Darlington, is a man who sees an absurd value in everything, and doesn’t know the market price of any single thing.

According to one of the participants at the PowerSwitch conference, some eCommerce sites quote higher prices for Apple users, based on the idea that they are less price-sensitive and can afford to pay more. In other words, the cynical Internet regards Apple users as sentimentalists.

If there is an alternative to this calculative thinking, it comes down to reestablishing trust. Perhaps then retailers and consumers alike can avoid an artificial choice between cynicism and sentimentalism.


Update (2020) added a link to a new paper by Frederik Borgesius, which looks at some of the legal as well as ethical implications of differential pricing.


Emma Brockes, I found something I like in a store. Is it wrong to buy it online for less? (Guardian, 3 May 2017)

Frederik Zuiderveen Borgesius, Price Discrimination, Algorithmic Decision-making, and European Non-discrimination Law (European Business Law Review, 2019/20)

Ariel Ezrachi and Maurice Stucke, Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy (Harvard University Press, 2016) - more links via publisher's page

Tim Harford, Exploitative algorithms are using tricks as old as haggling at the bazaar (Financial Times, 5 October 2018)

Laura James, Power Switch - Conference Report (31 March 2017). Further links including video via Power Switch Conference (March 2017)

Joshua Kopstein, Is Amazon Price-Gouging You? (Vocativ, 4 May 2017) via @charlesarthur

Jerry Useem, How Online Shopping Makes Suckers of Us All (Atlantic, May 2017)

Price-bots can collude against consumers (Economist, 6 May 2017)

The Dilemma of Showrooming, (Daniels Fund Ethics Initiative, University of New Mexico)


Related posts: Online pricing practices to be regulated? (October 2009), Predictive Showrooming (December 2012), Showrooming and Multi-Sided Markets (December 2012), Showrooming in the Knowledge Economy (December 2012), The Price of Fish (January 2013), Power Switch Conference (March 2017), The Idea of Showrooming (July 2017), Shoshana Zuboff on Surveillance Capitalism (February 2019)


Updated 11 July 2020

Thursday, March 09, 2017

Inspector Sands to Platform Nine and Three Quarters

Last week was not a good one for the platform business. Uber continues to receive bad publicity on multiple fronts, as noted in my post on Uber's Defeat Device and Denial of Service (March 2017). And on Tuesday, a fat-fingered system admin at AWS managed to take out a significant chunk of the largest platform on the planet, seriously degrading online retail in the Northern Virginia (US-EAST-1) Region. According to one estimate, performance at over half of the top internet retailers was hit by 20 percent or more, and some websites were completely down.

What have we learned from this? Yahoo Finance tells us not to worry.
"The good news: Amazon has addressed the issue, and is working to ensure nothing similar happens again. ... Let’s just hope ... that Amazon doesn’t experience any further issues in the near future."

Other commentators are not so optimistic. For Computer Weekly, this incident
"highlights the risk of running critical systems in the public cloud. Even the most sophisticated cloud IT infrastructure is not infallible."

So perhaps one lesson is not to trust platforms. Or at least not to practice wilful blindness when your chosen platform or cloud provider represents a single point of failure.

One of the myths of cloud, according to Aidan Finn,
"is that you get disaster recovery by default from your cloud vendor (such as Microsoft and Amazon). Everything in the cloud is a utility, and every utility has a price. If you want it, you need to pay for it and deploy it, and this includes a scenario in which a data center burns down and you need to recover. If you didn’t design in and deploy a disaster recovery solution, you’re as cooked as the servers in the smoky data center."

Interestingly, Amazon itself was relatively unaffected by Tuesday's problem. This may have been because they split their deployment across multiple geographical zones. However, as Brian Guy points out, there are significant costs involved in multi-region deployment, as well as data protection issues. He also notes that this question is not (yet) addressed by Amazon's architectural guidelines for AWS users, known as the Well-Architected Framework.

Amazon recently added another pillar to the Well-Architected Framework, namely operational excellence. This includes such practices as performing operations with code: in other words, automating operations as much as possible. Did someone say Fat Finger?




Abel Avram, The AWS Well-Architected Framework Adds Operational Excellence (InfoQ, 25 Nov 2016)

Julie Bort, The massive AWS outage hurt 54 of the top 100 internet retailers — but not Amazon (Business Insider, 1 March 2017)

Aidan Finn, How to Avoid an AWS-Style Outage in Azure (Petri, 6 March 2017)

Brian Guy, Analysis: Rethinking cloud architecture after the outage of Amazon Web Services (GeekWire, 5 March 2017)

Daniel Howley, Why you should still trust Amazon Web Services even though it took down the internet (Yahoo Finance, 6 March 2017)

Chris Mellor, Tuesday's AWS S3-izure exposes Amazon-sized internet bottleneck (The Register, 1 March 2017)

Shaun Nichols, Amazon S3-izure cause: Half the web vanished because an AWS bod fat-fingered a command (The Register, 2 March 2017)

Cliff Saran, AWS outage shows vulnerability of cloud disaster recovery (Computer Weekly, 6 March 2017)

Sunday, January 01, 2017

The Unexpected Happens

When Complex Event Processing (CEP) emerged around ten years ago, one of the early applications was real-time risk management. In the financial sector, there was growing recognition for the need for real-time visibility - continuous calibration of positions – in order to keep pace with the emerging importance of algorithmic trading. This is now relatively well-established in banking and trading sectors; Chemitiganti argues that the insurance industry now faces similar requirements.

In 2008, Chris Martins, then Marketing Director for CEP firm Apama, suggested considering CEP as a prospective "dog whisperer" that can help manage the risk of the technology "dog" biting its master.

But "dog bites master" works in both directions. In the case of Eliot Spitzer, the dog that bit its master was the anti money-laundering software that he had used against others.

And in the case of algorithmic trading, it seems we can no longer be sure who is master - whether black swan events are the inevitable and emergent result of excessive complexity, or whether hostile agents are engaged in a black swan breeding programme.  One of the first CEP insiders to raise this concern was John Bates, first as CTO at Apama and subsequently with Software AG. (He now works for a subsidiary of SAP.)

from Dark Pools by Scott Patterson

And in 2015, Bates wrote that "high-speed trading algorithms are an alluring target for cyber thieves".

So if technology is capable of both generating unexpected events and amplifying hostile attacks, are we being naive to imagine we use the same technology to protect ourselves?

Perhaps, but I believe there are some productive lines of development, as I've discussed previously on this blog and elsewhere.


1. Organizational intelligence - not relying either on human intelligence alone or on artificial intelligence alone, but looking for establishing sociotechnical systems that allow people and algorithms to collaborate effectively.

2. Algorithmic biodiversity - maintaining multiple algorithms, developed by different teams using different datasets, in order to detect additional weak signals and generate "second opinions".





John Bates, Algorithmic Terrorism (Apama, 4 August 2010). To Catch an Algo Thief (Huffington Post, 26 Feb 2015)

John Borland, The Technology That Toppled Eliot Spitzer (MIT Technology Review, 19 March 2008) via Adam Shostack, Algorithms for the War on the Unexpected (19 March 2008)

Vamsi Chemitiganti, Why the Insurance Industry Needs to Learn from Banking’s Risk Management Nightmares.. (10 September 2016)

Theo Hildyard, Pillar #6 of Market Surveillance 2.0: Known and unknown threats (Trading Mesh, 2 April 2015)

Neil Johnson et al, Financial black swans driven by ultrafast machine ecology (arXiv:1202.1448 [physics.soc-ph], 7 Feb 2012)

Chris Martins, CEP and Real-Time Risk – “The Dog Whisperer” (Apama, 21 March 2008)

Scott Patterson, Dark Pools - The Rise of A. I. Trading Machines and the Looming Threat to Wall Street (Random House, 2013). See review by David Leinweber, Are Algorithmic Monsters Threatening The Global Financial System? (Forbes, 11 July 2012)

Richard Veryard, Building Organizational Intelligence (LeanPub, 2012)

Related Posts

Black Swans and Complex Systems Failure (April 2011)
The Shelf-Life of Algorithms (October 2016)
Robust Against Manipulation (July 2019)

Wednesday, October 26, 2016

The Shelf-Life of Algorithms

@mrkwpalmer (TIBCO) invites us to take what he calls a Hyper-Darwinian approach to analytics. He observes that "many algorithms, once discovered, have a remarkably short shelf-life" and argues that one must be as good at "killing off weak or vanquished algorithms" as creating new ones.

As I've pointed out elsewhere (Arguments from Nature, December 2010), the non-survival of the unfit (as implied by his phrase) is not logically equivalent to the survival of the fittest, and Darwinian analogies always need to be taken with a pinch of salt. However, Mark raises an important point about the limitations of algorithms, and the need for constant review and adaptation, to maintain what he calls algorithmic efficacy.

His examples fall into three types. Firstly there are algorithms designed to anticipate and outwit human and social processes, from financial trading to fraud. Clearly these need to be constantly modified, otherwise the humans will learn to outwit the algorithms. And secondly there are algorithms designed to compete with other algorithms. In both cases, these algorithms need to keep ahead of the competition and to avoid themselves becoming predictable. Following an evolutionary analogy, the mutual adaptation of fraud and anti-fraud tactics resembles the co-evolution of predator and prey.

Mark also mentions a third type of algorithm, where the element of competition and the need for constant change is less obvious. His main example of this type is in the area of predictive maintenance, where the algorithm is trying to predict the behaviour of devices and networks that may fail in surprising and often inconvenient ways. It is a common human tendency to imagine that these devices are inhabited by demons -- as if a printer or photocopier deliberately jams or runs out of toner because it somehow knows when one is in a real hurry -- but most of us don't take this idea too seriously.

Where does surprise come from? Bateson suggests that it comes from an interaction between two contrary variables: probability and stability --
"There would be no surprises in a universe governed either by probability alone or by stability alone."
--  and points out that because adaptations in Nature are always based on a finite range of circumstances (data points), Nature can always present new circumstances (data) which undermine these adaptations. He calls this the caprice of Nature.
"This is, in a sense, most unfair. ... But in another sense, or looked at in a wider perspective, this unfairness is the recurrent condition for evolutionary creativity."

The problem with adaptation being based solely on past experience also arises with machine learning, which generally uses a large but finite dataset to perform inductive reasoning, in a way that is non-transparent to the human. This probably works okay for preventative maintenance on relatively simple and isolated devices, but as devices and their interconnections get more complex, we shouldn't be too surprised if algorithms, whether based on human mathematics or machine learning, sometimes get caught out by the caprice of Nature. Or by so-called Black Swans.

This potential unreliability is particularly problematic in two cases. Firstly, when the algorithms are used to make critical decisions affecting human lives - as in justice or recruitment systems. (See for example, Zeynap Tufekci's recent TED talk.) And secondly, when preventative maintenance has safety implications - from aeroengineering to medical implants.

One way of mitigating this risk might be to maintain multiple algorithms, developed by different teams using different datasets, in order to detect additional weak signals and generate "second opinions". And get human experts to look at the cases where the algorithms strongly disagree.

This would suggest that we maybe shouldn't be too hasty to kill off algorithms with poor efficacy, but sometimes keep them in the interests of algorithmic biodiversity.  (There - now I'm using the evolutionary metaphor.)



Gregory Bateson, "The New Conceptual Frames for Behavioural Research". Proceedings of the Sixth Annual Psychiatric Institute (Princeton NJ: New Jersey Neuro-Psychiatric Institute, September 17, 1958). Reprinted in G. Bateson, A Sacred Unity: Further Steps to an Ecology of Mind (edited R.E. Donaldson, New York: Harper Collins, 1991) pp 93-110

Mark Palmer, The emerging Darwinian approach to analytics and augmented intelligence (TechCrunch, 4 September 2016)

Zeynap Tufekci, Machine intelligence makes human morals more important (TED Talks, Filmed June 2016)


Related Posts
The Transparency of Algorithms (October 2016)

Sunday, August 07, 2016

Why does my bank need more personal data?

I recently went into a High Street branch of my bank and moved a bit of money between accounts. I could have done more, but I didn't have any additional forms of identification with me.

At the end, the cashier asked me for my nationality. British, as it happens. Why do you want to know? The cashier explained that this enabled a security control: if I ever bring my passport into a branch as a form of identification, the system can check that my passport matches my declared nationality.

Really? Really? If this is really a security measure, it's a pretty feeble one. Does my bank imagine I'm going to say I'm British and then produce a North Korean passport? Like a James Bond film?

After she had explained how the bank would use my nationality data, she then asked for my National Insurance number. I declined, choosing not to quiz her any further, and left the branch planning to write a stiff letter to the head of data protection at the bank's head office.

As a data expert, I am always a little suspicious of corporate motives for data collection. So the thought did occur to me that my bank might be planning to use my personal data for some purpose other than that stated.

Of course, my bank is perfectly entitled to collect data for marketing purposes, with my consent. But in this case, I was explicitly told that the data were being collected for a very narrowly defined security purpose.

So there are two possibilities. Either my bank doesn't understand security, or it doesn't understand data protection. (Of course there will be individuals who understand these things, but the bank as an organization appears to have failed to embed this understanding into its systems and working practices.) I shall be happy to provide advice and guidance on these topics.



Saturday, June 04, 2016

As How You Drive

I have been discussing Pay As You Drive (PAYD) insurance schemes on this blog for nearly ten years.

The simplest version of the concept varies your insurance premium according to the quantity of driving - Pay As How Much You Drive. But for obvious reasons, insurance companies are also interested in the quality of driving - Pay As How Well You Drive - and several companies now offer a discount for "safe" driving, based on avoiding events such as hard braking, sudden swerves, and speed violations.

Researchers at the University of Washington argue that each driver has a unique style of driving, including steering, acceleration and braking, which they call a "driver fingerprint". They claim that drivers can be quickly and reliably identified from the braking event stream alone.

Bruce Schneier posted a brief summary of this research on his blog without further comment, but a range of comments were posted by his readers. Some expressed scepticism about the reliability of the algorithm, while others pointed out that driver behaviour varies according to context - people drive differently when they have their children in the car, or when they are driving home from the pub.

"Drunk me drives really differently too. Sober me doesn't expect trees to get out of the way when I honk."

Although the algorithm produced by the researchers may not allow for this kind of complexity, there is no reason in principle why a more sophisticated algorithm couldn't allow for it. I have long argued that JOHN-SOBER and JOHN-DRUNK should be understood as two different identities, with recognizably different patterns of behaviour and risk. (See my post on Identity Differentiation.)

However, the researchers are primarily interested in the opportunities and threats created by the possibility of using the "driver fingerprint" as a reliable identification mechanism.

  • Insurance companies and car rental companies could use "driver fingerprint" data to detect unauthorized drivers.
  • When a driver denies being involved in an incident, "driver fingerprint" data could provide relevant evidence.
  • The police could remotely identify the driver of a vehicle during an incident.
  • "Driver fingerprint" data could be used to enforce safety regulations, such as the maximum number of hours driven by any driver in a given period.

While some of these use cases might be justifiable, the researchers outline various scenarios where this kind of "fingerprinting" would represent an unjustified invasion of privacy, observe how easy it is for a third party to obtain and abuse driver-related data, and call for a permission-based system for controlling data access between multiple devices and applications connected to the CAN bus within a vehicle. (CAN is a low-level protocol, and does not support any security features intrinsically.)


Sources

Miro Enev, Alex Takakuwa, Karl Koscher, and Tadayoshi Kohno, Automobile Driver Fingerprinting Proceedings on Privacy Enhancing Technologies; 2016 (1):34–51

Andy Greenberg, A Car’s Computer Can ‘Fingerprint’ You in Minutes Based on How You Drive (Wired, 25 May 2016)

Bruce Schneier, Identifying People from their Driving Patterns (30 May 2016)

See also John H.L. Hansen, Pinar Boyraz, Kazuya Takeda, Hüseyin Abut, Digital Signal Processing for In-Vehicle Systems and Safety. Springer Science and Business Media, 21 Dec 2011

Wikipedia: CAN bus, Vehicle bus


Related Posts

Identity Differentiation (May 2006)

Pay As You Drive (October 2006) (June 2008) (June 2009)

Monday, March 18, 2013

Cloud and Continuity of Supply Risk

@dougnewdick points out the risk of a company becoming over-dependent on Google. His particular example is prompted by Google's announcement that Google Reader will be discontinued.

I have previously commented on the subject of Creeping Business Dependency, the fact that many companies have allowed themselves to become dependent on a particular company, product or technology. Especially Google. If Google decides your website offends against some search engine rules, it is perfectly capable of making your website disappear from searches. (BMW disappeared from Google for three days in 2006 - see my post BMW Search Requests.) A company might well go bust before it could sort the problem out.

Of course, you can't avoid some dependencies, but I think it is important that any significant dependency should be clearly visible in the business architecture. (In general, business architects usually neglect this kind of dependency until I point out specific examples to them.)

When looking at this kind of dependency, it is important to remember the principles of asymmetry - the Product is not the Technology, and the Company is not the Product. There have been a few popular products and platforms whose owners lost interest - these included Bloglines (formerly owned by Ask) and Delicious (formerly owned by Yahoo) - but were revived under new ownership. Users of a popular platform may feel that a large user base provides grounds for optimism that someone will want to keep it going, even if the original owner doesn't wish to. However, there are many products and platforms that have not survived.

More fundamental is the question of the underlying technology. A few years ago, there was considerable confidence and investment in RSS and Atom feeds, and a number of products and platforms were developed to exploit this technology. If there is a healthy ecosystem of different products and platforms, with relatively low switching costs, it doesn't matter much if one product drops out. But if Google and others are losing interest in this technology, that's a much more fundamental problem for anyone who is heavily committed to it.

If Google stops providing a free service, those who really want it may have to pay to get a decent service elsewhere. But this alters the economics of the service ecosystem, with unpredictable consequences. Clearly there is a risk that the service you want (or the service you need your customers to use) is increasingly expensive, inconvenient and ultimately unavailable.


Doug Newdick, Cloud and Continuity of Supply Risk (March 2013)

Saturday, February 18, 2012

BYOD - Bring Your Own Device

By popular demand, many companies are shifting ownership of elements of corporate infrastructure onto their employees. This is known as BYOC (bring your own computer) or BYOD (bring your own device).

There are many aspects to this trend.

1. Culture. Talented recruits may see this kind of choice as a desirable feature of a future employer. Some of them may have a strong personal commitment to a particular device; others may ask about BYOD policy as a quick way of getting a general impression of company culture and its attitude towards employees.

(Even if BYOD is a common request at interview, this doesn't mean it is a genuine requirement. In some cases, the BYOD request could be similar to the apparently crazy riders that performers may add to contracts as a way of testing the diligence and attention to detail of the organizers. The best-known example of such a contract rider is Van Halen's insistence on a bowl of MnMs with the brown ones removed. See "Brown out" at snopes.com.)

2. Interoperability. There is a need for interoperability within the enterprise (endo-interoperability) as well as interoperability with external platforms (exo-interoperability). Within the enterprise, people expect to be able to use common services (email, communications, content management, and so on) regardless of device. When I'm in the office, I want to be able to connect my device to office devices such as printers and projectors, as well as using the office network and servers. When I'm working at home, I want to be able to connect my device into the office systems, and use my device for web conferences and other events. But I also want to be able to connect my device into public platforms such as Facebook.


3. Innovation. Early adopters like to carry the latest and most fashionable device, even if this doesn't yet support all the required corporate services in a robust manner.

4. Business continuity and risk. A person's productivity can be seriously impaired if the device is lost or develops a fault. Conversely, a company's security can be seriously impaired if an employee uses an unverified emergency device such as her teenage son's phone. Does BYOD imply the rapid availability of backup devices of every conceivable brand, or does the company provide a limited range of standard devices for emergency use?

5. Support. Does the device deliver all the required corporate services correctly, efficiently and securely? Whose responsibility is it to verify and test these services on the given device, and to sort out the (inevitable) configuration problems? What knowledge and expertise is needed to provide adequate support across the full range of devices?

6. Economics. Device provision within large organizations was traditionally based on the economics of scale. We purchase thousands of identical devices, install the same software and services on each one, and issue these to our employees. We can obtain good discounts from the hardware and software suppliers, and we can train our support staff to provide efficient support across a narrow range of products. But this approach fails to deal with the complexities of the modern business organization where each employee has different needs, often calling for additional non-standard software and services, or even newer devices. So most modern organizations shift to provision of devices based on the economics of scope - giving everyone a flexible device platform to which additional software and services can be easily added. Then the move to BYOD takes us into the economics of alignment - optimizing the lifetime cost of device provision against the lifetime benefits to the organization and the individual within the context of use.

7. BYOD represents a shift in the balance between two kinds of device vendor - the ones who sell thousands of devices at a time by schmoozing the CIO and the ones who sell devices to individuals via consumer channels. (As a result, some stakeholders may be cynical and unsympathetic to any objection to BYOD from the CIO quarter.)

8. More fundamentally, BYOD represents a shift in the balance of power between two kinds of knowledge. The corporate IT folk supposedly know more about the corporate services and about quality attributes such as reliability and security. However, the individual employee knows more about the context of use. The architectural question here is aligning the device selection, configuration and use with the emerging requirements of the individual in the job. This is ultimately a question of governance, which needs to be guided by appropriate BYOD policies.


A lot of architectural issues then.


Fiona Graham, BYOC: Should employees buy their own computers? (BBC News 14 January 2011)

Fiona Graham, BYOD: Bring your own device could spell end for work PC (BBC News 14 February 2012)

Eric Vanderburg, Four Keys to Successful BYOD (CIO 14 February 2012)


Related posts:

Bring your own expectations (May 2014)

Thursday, January 05, 2012

Unruly Google and VPEC-T

Google has been hoist by its own petard: it seems obliged to ban its own browser from its own search engine for infringing its strict rules. Apparently the infringement resulted from some misbehaviour somewhere down the subcontract chain, unknown to Google itself or its prime subcontractor (which with fitting irony is called Unruly Media). A number of blogposts were created to promote Google Chrome, containing direct hotlinks to the Chrome download page. Google has recently penalized a number of other companies for such behaviour, including J C Penney, Forbes and Overstock. See also my 2006 post on BMW Search Requests.

A number of offending posts were discovered because they contained the magic words This post was sponsored by Google, and the Google search engine dutifully delivered a list of webpages containing these words. (This kind of transparency was foreseen by Isaac Asimov in a story called "All the troubles of the world", in which the computer Multivac was unable to conceal its own self-destructive behaviour.)

As a number of search engine analysts have pointed out, there are two problems with the sponsored pages. Besides containing the offending links, they are also pretty thin in terms of content. (Google has recently developed a search filter code-named Panda, which is intended to demote such low-value content, but this filter is extremely costly in computing power and is apparently only run sporadically.) Many of these pages credit Google Chrome for having helped a company in Vermont over the past five years, despite the fact that Google Chrome hasn't been available for that long. None of them explain why Google Chrome might be better than other browsers.

So here we have an interesting interaction between the elements of VPEC-T. 


Value - How is commercial sponsorship reconciled with high-value content? Does this incident expose a conflict of interest inside Google?

Policy - How does Google apply its strict rules to itself?

Events - How was this situation detected (with the aid of Google itself)? Will any future incidents be as easy to detect?

Content - What is the net effect on the content, on which Google's market position depends?

Trust - What kinds of trust have been eroded in this situation? How can trust be restored, and how long will it take?



Sources


Aaron Wall, Google caught buying paid links yet again (SEO Book 2 Jan 2012)

Danny Sullivan, Google’s Jaw-Dropping Sponsored Post Campaign For Chrome (SearchEngineLand 2 Jan 2012)

Charles Arthur, Will Google be forced to ban its own browser from its index? (Guardian 3 Jan 2012) Google shoves Chrome down search rankings after sponsored blog mixup (Guardian 4 Jan 2012)

 

Related post: Towards a VPEC-T analysis of Google (October 2011)

Tuesday, November 29, 2011

Risk and Responsibility in Self-Service

A cabbie asked @jkuramot to enter his destination into the GPS. @dahowlett suggests this is because he didn't speak good English. @jkuramot confirms that the driver didn't speak English very well but adds that "this was his go-to move".

The reason we are talking about this fragment of service design is that it is unusual in this context: we normally expect the driver to enter the destination into his navigation device. But the normal procedure is prone to error; the passenger may not speak clearly, the driver may not understand correctly, there may be a lot of background noise: the passenger arrives at the wrong destination and it's the driver's fault.

However, if the passenger enters the destination directly into the navigation device, then any error is the passenger's fault. Many service providers in other areas now follow this pattern; shifting responsibility onto the customer may help to reduce administration costs, but more importantly reduces the service provider's liability. But if the customer is not able to perform these tasks easily and accurately, this kind of shift adds more to the cost and risk for the customer than it reduces for the supplier, and therefore diminishes total value. See my review of The Support Economy.

Asking the customer to do the work makes an assumption about the customer's capability. I don't know Jake personally, but he looks from his photo and his Twitter profile like someone who would know how to operate this kind of device. The driver may have had the same impression; it is conceivable that he would have treated Jake's grandmother differently. Whereas if the device (belonging to the driver) is unusual and difficult to use, we would always insist that the driver should operate it. Self-service only works if the interface design offers a reasonable level of usability.

The other difference between the passenger and the driver is the question of which is more familiar with the destination. When I get a cab home from the airport, obviously I know my address better than the driver does. But when I arrive in a strange city, I expect the cab drivers to be more familiar with the hotels than I am: if I get the name of the hotel slightly wrong, the driver should ask if I really meant something else, rather than drive for an hour to a hotel in the next city whose name exactly matches what I said.

By the way, Google has been correcting our searches for a long time now, but has now chosen to issue a series of advertisements in which this correction (and the collection of vast amounts of data to make this correction possible) is highlighted as a service enhancement feature. See my note Towards a VPEC-T analysis of Google. This kind of service enhancement is unavailable if the driver takes himself out of the loop, and regards his job as merely enacting a specification agreed between the customer and an electronic device.

Wednesday, April 06, 2011

Black Swans and Complex System Failure

Black Swan theory (Wikipedia) tells us among other things that people tend to underestimate the probability of extremely rare events.

A corollary of this theory that is of particular interest to architects and complex system engineers concerns the design of fail-safe mechanisms. Nuclear power and oil extraction are examples of environmentally critical operations; they are therefore subject to detailed risk assessment, and designed with multiple fail-safe mechanisms. And yet both the oil spillage last year in the Gulf of Mexico and the partial melt-down in Japanese nuclear reactors following the recent tsunami involved the simultaneous failure of multiple fail-safe mechanisms. Obviously that's not supposed to happen.

Simultaneous failure of supposedly independent mechanisms is a Black Swan event.


Update (August 2011)

A recent study by Oxford University and McKinsey has blamed rare but high-impact problems, dubbed "black swans", for the increasingly common phenomenon of large IT project whose cost spirals out of control. The study finds this phenomenon to be three times as common in IT than in other domains [BBC News, 26 August 2011]. See my post on Black Swan Blindness.



Update (October 2011)

Reviewing a couple of recent books about BP and the oil spill in the Gulf of Mexico, Mattathias Schwartz makes a number of relevant points.

When crucial pieces of our infrastructure fail, they do so gracelessly, without much warning and in ways that are difficult to anticipate. ... The failure to grasp the possibility of system-wide failure might be one in an accelerating series, bookended by the 2008 financial crisis and the Fukushima nuclear meltdown last spring.
One reason for the oil and gas industry’s quick comeback in the US was the successful packaging of the blowout as a ‘black swan’, an event of such low probability that it couldn’t have been anticipated. This certainly helped excuse the fact that no one – not BP, Chevron, Exxon or Shell – had a working plan for plugging a blowout as deep as Macondo .
BP ... claimed, in its own report on the blowout, that the event had eight causes, of which BP was partly responsible for one. The president’s commission concluded that the disaster had nine causes, and that BP was responsible for six or seven. And yet BP stands by what it said at the start. 

The size of the system and the complexity of the data make it possible to argue for a maddeningly wide range of positions, especially when it comes to vague legal notions like ‘negligence’ or ‘responsibility’. Both concepts hinge on proving that one linear narrative is the right one. 

Mattathias Schwartz, LRB 6 October 2011 
reviewing
  • Spills and Spin: The Inside Story of BP by Tom Bergin 
  • A Hole at the Bottom of the Sea: The Race to Kill the BP Oil Gusher by Joel Achenbach

Tuesday, March 08, 2011

Creeping Business Dependency

People are slowly waking up to the fact that we have created yet another single point of failure into our business ecosystem. It seems that businesses have gradually made themselves dependent on Global Positioning Systems (GPS) and satellite navigation (satnav). So we are now starting to hear doom-and-gloom stories about the dire economic consequences of any interruption to the service, which could apparently be caused by anything from cyberterrorism (Daily Mail 8 March 2011) to solar flares (Daily Mail 21 Sept 2010).

Those with long memories may recall the millennium bug scare, which postulated that widespread computer error might result in total economic collapse when the date went from 99 to 00. Many companies took the opportunity to carry out a long overdue inventory of their software programs, and decommissioned a fair amount of obsolete code, as well as reviewing their disaster recovery procedures; even though the scare was probably exaggerated, some useful work was done. (I myself picked up some contract work in this area, so I can't complain.)

The Royal Academy of Engineering has just issued a report on Global Navigation Space Systems, which takes a more balanced view of the subject than the Daily Mail, but still warns of the danger of over-reliance on satellite navigation [Report (pdf), Press Release].

Chairman of the RAoE working group, Dr Martyn Thomas, told the BBC:
"We're not saying that the sky is about to fall in; we're not saying there's a calamity around the corner. What we're saying is that there is a growing interdependence between systems that people think are backing each other up. And it might well be that if a number these systems fail simultaneously, it will cause commercial damage or just conceivably loss of life. This is wholly avoidable." [BBC News 8 March 2011]

Maybe this does sound pretty speculative (as @martinjmurray complains). Nonetheless it may be a good idea for any business that has gradually become dependent on this or any other technology to check out the possible risks.

From an architectural point of view, what I find most interesting about this situation is the tendency for critical business dependencies (and the associated risks) to emerge, as a particular technology migrates unobtrusively from marginal use to core business use.

Another example of a creeping business dependency is the extent to which Google has now inserted itself into the relationship between any business and its customers. If a business offends Google in some way, and consequently disappears from Google search, this will have serious business consequences. (BMW disappeared from Google for three days in 2006 - see my post BMW Search Requests). And yet it's still rare to see Google shown as a business-critical service partner in business architecture or business process diagrams.

If we think of an architecture in terms of a set of dependencies, we can distinguish between a centrally planned architecture, in which the dependencies and their implications are understood from the outset, and an emergent defacto architecture, in which unanticipated dependencies and risks can be created by a quantity of uncontrolled activity. In a planned world, all innovation must be controlled to prevent emergent risk; in an evolving world, innovation (such as the use of Google or GPS) can be encouraged provided that there is a robust mechanism to detect and manage emerging risks.


Related posts: BMW Search Requests (Feb 2006), Cloud and Continuity of Supply Risk (March 2013)

Sunday, May 16, 2010

SOA and Risk Management

#soa #risk In this post, I identify some contrasting views on the relationship between SOA and risk.

SOA involves innovation, and innovation always introduces new risk


SOA helps reduce risk


    SOA providing visibility and control of aggregate risk and unexpected behaviour


    SOA complicating visibility and control of aggregate risk


    Therefore ... risk management as one area likely to see spending increasing


    Oh yeah? Any evidence of this?

    Friday, April 23, 2010

    Architect Certification and Trust

    @mattdeacon @wendydevolder @karianna @flowchainsensei @gojkoadzic @unclebobmartin .

    Lots of good comments on Twitter and elsewhere about certification, in various contexts (enterprise architecture, agile, ...).

    The purpose of a certificate is to enable you to trust the bearer with something. So we need to understand the nature of trust. In their book Trust and Mistrust, my friends Aidan Ward and John Smith identify four types of trust ...
    • authority
    • network
    • commodity
    • authentic
    ... and we can apply these four types to the different styles of certification that might be available.

    In his attack on the World Agile Qualifications Board, @gojkoadzic quotes the Agile Alliance position on certification: employers should have confidence only in certifications that are skill-based and difficult to achieve. Yet, as Gojko continues, "most of the certificates issued today are very easy to achieve and take only a day or two of work, or even just attending the course".

    If a certificate is issued by a reputable professional organization, then the value of the certificate is underwritten by the reputation of the issuing organization, so this counts as authority trust. In my post Is Enterprise Architecture a Profession? I have already stated my view that claims for professional status for enterprise architecture are at best premature, so there is no organization today that has sufficient authority to issue certificates of professional competence. However, if you can acquire a certificate simply by attending a short course and/or memorizing some document (such as TOGAF), then this is a commodity-based form of trust. Basically, such certificates will only be regarded as valuable if just enough people have them. (Which seems to be why some large consultancies have put all their practitioners through TOGAF training.)

    Bob Marshall (@flowchainsensei) prefers vouching

    Just found http://wevouchfor.org - Should keep me busy vouching (why oh why "certifying???") for capable folks for some time.

    which is a form of network trust. If someone receives a lot of vouchers from his friends, that could either mean he is very popular or that he is involved in a lot of reciprocal back-scratching. (This kind of mutual recommendation is easily visible on Linked-In, where the list of incoming recommendations often exactly matches the list of outgoing recommendations.)

    The trouble with all these mechanisms is that they are both one-sided and lacking context. The certificate purports to tell us about a person's strengths (but not weaknesses), in some unspecified or generic arena. This can only go so far in supporting a judgement about a person's qualifications (strengths and weaknesses) for a specific task in a specific context. What if anything would serve as an authentic token of trust?


    Aidan Ward and John Smith, Trust and Mistrust - Radical Risk Strategies in Business Relationships. John Wiley, 2003

    Tuesday, March 09, 2010

    Multiple styles of EA

    @tetradian has an interesting post on Big EA, Little EA and Personal EA., based loosely on Patti Ancram's classification of knowledge management.
    • Big KM is about top-down, structured and organizationally distinct “knowledge management”
    • Little KM is about safe-fail experiments embedded in the organizational structure
    • Personal KM is about access to tools and methods to ensure that knowledge, context, bits, fragments, thoughts, ideas are harvestable



    As I see it, this classification identifies different styles that may possibly coexist, or perhaps different kinds of knowledge claim that may interact in interesting ways. (I don't like the word "layers" for this kind of classification, because it implies a particular structural pattern, which isn't appropriate here.)

    I've used a slightly different division in the trust sphere, which might make sense here as well.
    • Authority EA - this is a kind of top-down command-and-control EA, representing the will-to-power of the enterprise as a whole, and ultimately answerable to the CEO. This is what Tom calls Big EA.
    • Commodity EA - this is where the EA is based on some kind of external product source - such as when the enterprise models are imported wholesale from IBM or SAP. This often resembles Big EA, but has some important differences.
    • Network EA - this is where EA is based on informal and emergent collaboration between people and organizations. Tom calls it Little EA, but the collaborations can be very extended indeed - just think about some of the mashup ecosystems around Google or Twitter.
    • Authentic EA - this is a personally engaged practice - what Tom calls Personal EA.

    Once we have agreed that there are different styles, the really interesting question is not identifying and naming the styles, nor even saying that one style is somehow "better" than another style", but talking about how the different styles interact, and what are the implications for governance.