Monday, June 09, 2014
Measuring the “harm” of a security breach.
PayTime Data Breach Hits Some Workers Hard
When we think about consequences of hacks or breaches, let’s not lose sight that people may lose their jobs simply because their data was caught up in an incident – even if there was no evidence that their information was misused. idRADAR.com has a good example of that in the aftermath of the PayTime hack. They previously reported other examples of how becoming a victim of hack can cost security clearance and/or jobs, with a follow-up on one such case.
I don't think I'd put it that way. Still, some interesting assertions.
Upsurge in hacking makes customer data a corporate time bomb
… The reality, cyber security experts say, is that however much they spend, even the largest companies are unlikely to be able to stop their systems being breached. The best defense may simply be either to reduce the data they hold or encrypt it so well that if stolen it will remain useless. [Or take most of it off-line? Bob]
… A report from cyber security think tank the Ponemon Institute showed the average cost of a data breach in the last year grew by 15 percent to $3.5 million. The likelihood of a company having a data breach involving 10,000 or more confidential records over a two-year period was 22 percent, it said.
… Still, a study of 102 UK financial institutions and 151 retail organizations conducted earlier this year by Tripwire showed 40 percent said they would need 2 to 3 days to detect a breach.
So, if I search for information on a company and Google indicates they have “something to hide,” I will expand my search by using search engines that do not comply with the EU rule. Or I may just not invest in that company. (Imagine the impact on politicians!)
Google may soon let you know when it’s required to hide something from you
A European Union court recently ruled that Google must respect the EU’s “right to be forgotten” and remove links to web pages that individuals find embarrassing.
Now, the Guardian reports, Google may soon add a note to its edited search results, indicating that something is missing.
Google already does this with pages from which it’s removed search results in response to DMCA takedown requests, usually as a result of alleged copyright violations.
Bootstrapping Privacy Compliance in Big Data Systems
by Sabrina I. Pacifici on June 8, 2014
“In this paper, we demonstrate a collection of techniques to transition to automated privacy compliance compliance checking in big data systems. To this end we designed the LEGALEASE language, instantiated for stating privacy policies as a form of restrictions on information flows, and the GROK data inventory that maps low level data types in code to highlevel policy concepts. We show that LEGALEASE is usable by non-technical privacy champions through a user study. We show that LEGALEASE is expressive enough to capture real-world privacy policies with purpose, role, and storage restrictions with some limited temporal properties, in particular that of Bing and Google. To build the GROK data flow grap we leveraged past work in program analysis and data flow analysis. We demonstrate how to bootstrap labeling the graph with LEGALEASE policy datatypes at massive scale. We note that the structure of the graph allows a small number of annotations to cover a large fraction of the graph. We report on our experiences and learnings from operating the system for over a year in Bing. — Shayak Sen (Carnegie Mellon University), Saikat Guha (Microsoft Research, India), Anupam Datta (Carnegie Mellon University), Sriram Rajamani (Microsoft Research, India), Janice Tsai (Microsoft Research, Redmond), and Jeannette Wing (Microsoft Research), Bootstrapping Privacy Compliance in Big Data Systems, IEEE Security and Privacy Symposium 2014, Best Student Paper (1 of 2) – See more at: https://www.cylab.cmu.edu/news_events/news/2014/ieee-sp-2014.html#sthash.eM6ZYdS3.dpuf”
Another programming inspired paper?
Location Tracking, Mosaic Theory, and Machine Learning
by Sabrina I. Pacifici on June 8, 2014
Enough is Enough - Location Tracking, Mosaic Theory, and Machine Learning - Steven M. Bellovin, Renée M. Hutchins, Tony Jebara, Sebastian Zimmeck. New York University Journal of Law & Liberty, vol 8:555, 2014.
“Since 1967, when it decided Katz v. United States, the Supreme Court has tied the right to be free of unwanted government scrutiny to the concept of reasonable expectations of privacy. An evaluation of reasonable expectations depends, among other factors, upon an assessment of the intrusiveness of government action. When making such assessment historically the Court has considered police conduct with clear temporal, geographic, or substantive limits. However, in an era where new technologies permit the storage and compilation of vast amounts of personal data, things are becoming more complicated. A school of thought known as “mosaic theory” has stepped into the void, ringing the alarm that our old tools for assessing the intrusiveness of government conduct potentially undervalue privacy rights. Mosaic theorists advocate a cumulative approach to the evaluation of data collection. Under the theory, searches are “analyzed as a collective sequence of steps rather than as individual steps.” The approach is based on the recognition that comprehensive aggregation of even seemingly innocuous data reveals greater insight than consideration of each piece of information in isolation. Over time, discrete units of surveillance data can be processed to create a mosaic of habits, relationships, and much more. Consequently, a Fourth Amendment analysis that focuses only on the government’s collection of discrete units of trivial data fails to appreciate the true harm of long-term surveillance — the composite. In the context of location tracking, the Court has previously suggested that the Fourth Amendment may (at some theoretical threshold) be concerned with the accumulated information revealed by surveillance. Similarly, in the Court’s recent decision in United States v. Jones, a majority of concurring justices indicated willingness to explore such an approach. However, in general, the Court has rejected any notion that technological enhancement matters to the constitutional treatment of location tracking. Rather, it has found that such surveillance in public spaces, which does not require physical trespass, is equivalent to a human tail and thus not regulated by the Fourth Amendment. In this way, the Court has avoided quantitative analysis of the amendment’s protections. The Court’s reticence is built on the enticingly direct assertion that objectivity under the mosaic theory is impossible. This is true in large part because there has been no rationale yet offered to objectively distinguish relatively short-term monitoring from its counterpart of greater duration. As Justice Scalia recently observed in Jones: “it remains unexplained why a 4-week investigation is ‘surely’ too long.” This article suggests that by combining the lessons of machine learning with the mosaic theory and applying the pairing to the Fourth Amendment we can see the contours of a response. Machine learning makes clear that mosaics can be created. Moreover, there are also important lessons to be learned on when that is the case… In five parts, this article advances the conclusion that the duration of investigations is relevant to their substantive Fourth Amendment treatment because duration affects the accuracy of the predictions. Though it was previously difficult to explain why an investigation of four weeks was substantively different from an investigation of four hours, we now have a better understanding of the value of aggregated data when viewed through a machine learning lens. In some situations, predictions of startling accuracy can be generated with remarkably few data points.”
See also a rebuttal of interpretations of this paper by Orin Kerr – No, machine learning doesn’t resolve how the mosaic theory applies