Privacy-preserving distributed k-anonymity

k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. This paper presents a k-anonymity protocol when the data is vertically partitioned between sites. A key contribution is a proof that the protocol preserves k-anonymity between the sites: While one site may have individually identifiable data, it learns nothing that violates k-anonymity with respect to the data at the other site. This is a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy

Blinded Fault Resistant Exponentiation

As the core operation of many public key cryptosystems, group exponentiation is central to cryptography. Attacks on its implementation in embedded device setting is hence of great concern. Recently, implementations resisting both simple side-channel analysis and fault attacks were proposed. In this paper, we go further and present an algorithm that also inherently thwarts differential side-channel attacks in any finite abelian group with only limited time and storage overhead

Random Cayley Digraphs and the Discrete Logarithm

Abstract. We formally show that there is an algorithm for dlog over all abelian groups that runs in expected optimal time (up to logarithmic factors) and uses only a small amount of space. To our knowledge, this is the first such analysis. Our algorithm is a modification of the classic Pollard rho, introducing explicit randomization of the parameters for the updating steps of the algorithm, and is analyzed using random walks with limited independence over abelian groups (a study which is of its own interest). Our analysis shows that finding cycles in such large graphs over groups that can be efficiently locally navigated is as hard as dlog.