On the Measurement of Privacy as an Attacker's Estimation Error

A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy enhancing-technologies. Most of these metrics are specific to concrete systems and adversarial models, and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist systems designers in selecting the most appropriate metric for a given application. In this work we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability and Bayes decision.Comment: This paper has 18 pages and 17 figure

Revisiting distance-based record linkage for privacy-preserving release of statistical datasets

Statistical Disclosure Control (SDC, for short) studies the problem of privacy-preserving data publishing in cases where the data is expected to be used for statistical analysis. An original dataset T containing sensitive information is transformed into a sanitized version T' which is released to the public. Both utility and privacy aspects are very important in this setting. For utility, T' must allow data miners or statisticians to obtain similar results to those which would have been obtained from the original dataset T. For privacy, T' must significantly reduce the ability of an adversary to infer sensitive information on the data subjects in T. One of the main a-posteriori measures that the SDC community has considered up to now when analyzing the privacy offered by a given protection method is the Distance-Based Record Linkage (DBRL) risk measure. In this work, we argue that the classical DBRL risk measure is insufficient. For this reason, we introduce the novel Global Distance-Based Record Linkage (GDBRL) risk measure. We claim that this new measure must be evaluated alongside the classical DBRL measure in order to better assess the risk in publishing T' instead of T. After that, we describe how this new measure can be computed by the data owner and discuss the scalability of those computations. We conclude by extensive experimentation where we compare the risk assessments offered by our novel measure as well as by the classical one, using well-known SDC protection methods. Those experiments validate our hypothesis that the GDBRL risk measure issues, in many cases, higher risk assessments than the classical DBRL measure. In other words, relying solely on the classical DBRL measure for risk assessment might be misleading, as the true risk may be in fact higher. Hence, we strongly recommend that the SDC community considers the new GDBRL risk measure as an additional measure when analyzing the privacy offered by SDC protection algorithms.Postprint (author's final draft

Information theoretical cryptogenography

We consider problems where n people are communicating and a random subset of them is trying to leak information, without making it clear who are leaking the information. We introduce a measure of suspicion and show that the amount of leaked information will always be bounded by the expected increase in suspicion, and that this bound is tight. Suppose a large number of people have some information they want to leak, but they want to ensure that after the communication, an observer will assign probability at most c to the events that each of them is trying to leak the information. How much information can they reliably leak, per person who is leaking? We show that the answer is (−log(1−c)c−log(e)) bits

Quantifying Side-Channel Information Leakage from Web Applications

Recent research has shown that many popular web applications are vulnerable to side-channel attacks on encrypted streams of network data produced by the interaction of a user with an application. As a result, private user data is susceptible to being recovered by a side-channel adversary. A recent focus has been on the development of tools for the detection and quantification of side-channel information leaks from such web applications. In this work we describe a model for these web applications, analyse the effectiveness of previous approaches for the quantification of information leaks, and describe a robust, effective and generically applicable metric based on a statistical estimation of the mutual information between the user inputs made in the application and subsequent observable side-channel information. We use our proposed metric to construct a test capable of analysing sampled traces of packets to detect information leaks, and demonstrate the application of our test on a real-world web application

A Transaction-Level Model for Blockchain Privacy

Considerable work explores blockchain privacy notions. Yet, it usually employs entirely different models and notations, complicating potential comparisons. In this work, we use the Transaction Directed Acyclic Graph (TDAG) and extend it to capture blockchain privacy notions (PDAG). We give consistent definitions for untraceability and unlinkability. Moreover, we specify conditions on a blockchain system to achieve each aforementioned privacy notion. Thus, we can compare the two most prominent privacy-preserving blockchains -- Monero and Zcash, in terms of privacy guarantees. Finally, we unify linking heuristics from the literature with our graph notation and review a good portion of research on blockchain privacy