Quantitative Information Flow and Applications to Differential Privacy

International audienceSecure information flow is the problem of ensuring that the information made publicly available by a computational system does not leak information that should be kept secret. Since it is practically impossible to avoid leakage entirely, in recent years there has been a growing interest in considering the quantitative aspects of information flow, in order to measure and compare the amount of leakage. Information theory is widely regarded as a natural framework to provide firm foundations to quantitative information flow. In this notes we review the two main information-theoretic approaches that have been investigated: the one based on Shannon entropy, and the one based on Rényi min-entropy. Furthermore, we discuss some applications in the area of privacy. In particular, we consider statistical databases and the recently-proposed notion of differential privacy. Using the information-theoretic view, we discuss the bound that differential privacy induces on leakage, and the trade-off between utility and privac

Efficient algorithms for distortion and blocking techniques in association rule hiding

Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed in this context in order to extract this information in the most efficient way. However, efficiency is not our only concern in this study. The security and privacy issues over the extracted knowledge must be seriously considered as well. By taking this into consideration, we study the procedure of hiding sensitive association rules in binary data sets by blocking some data values and we present an algorithm for solving this problem. We also provide a fuzzification of the support and the confidence of an association rule in order to accommodate for the existence of blocked/unknown values. In addition, we quantitatively compare the proposed algorithm with other already published algorithms by running experiments on binary data sets, and we also qualitatively compare the efficiency of the proposed algorithm in hiding association rules. We utilize the notion of border rules, by putting weights in each rule, and we use effective data structures for the representation of the rules so as (a) to minimize the side effects created by the hiding process and (b) to speed up the selection of the victim transactions. Finally, we study the overall security of the modified database, using the C4.5 decision tree algorithm of the WEKA data mining tool, and we discuss the advantages and the limitations of blocking

Provably Secure Steganography with Imperfect Sampling

The goal of steganography is to pass secret messages by disguising them as innocent-looking covertexts. Real world stegosystems are often broken because they make invalid assumptions about the system’s ability to sample covertexts. We examine whether it is possible to weaken this assumption. By modeling the covertext distribution as a stateful Markov process, we create a sliding scale between real world and provably secure stegosystems. We also show that insufficient knowledge of past states can have catastrophic results

Computing the Leakage of Information-Hiding Systems

Contains fulltext : 83593.pdf (preprint version ) (Open Access

Flow schematic summarizing results of GBM-induced genes in co-cultured human myeloid cells.

<p>The number of genes satisfying each selection criteria are summarized, resulting in two gene subsets: those affected by LPS stimulation as well as culture with GBM tumor cells but not non-transformed human astrocytes, exemplified by AXL, and those unaffected by LPS stimulation but affected by GBM tumor cells, exemplified by CAV1. Additional genes within these two gene susbsets included CDCP1, CKS2, STC1, KRT18, and PHLDA2, but their differential expression could not be independently replicated (data not shown).</p