Privacy preserving data classification using inner product encryption

Conference of 12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016 ; Conference Date: 10 October 2016 Through 12 October 2016; Conference Code:193369International audienceIn the context of data outsourcing more and more concerns raise about the privacy of user’s data. One solution is to outsource the data in encrypted form. Meanwhile obtaining a service based on machine learning predictions on user data remains very important in real-life situations. This paper presents ways to combine machine learning algorithms and IPE in order to perform classification on encrypted data. The proposed privacy preserving classification schemes allow to keep user’s data encrypted but at the same time revealing to a server classification results on this data. We study the performance of such classification schemes and their information leakage

Chemically modified exopolysaccharide pullulans: physico-chemical characteristics of ionic derivatives

International audienceThe paper presents some physico-chemical characteristics of dilute solutions of exopolysaccharide pullulan and its carboxymethyl, sulfoethyl and sulfopropyl ionic derivatives.On-line size exclusion chromatography/multi-angle laser light scattering (SEC/ MALLS) was used for establishing their weight average molecular weight (Mw), number average molecular weight (Mn), polydispersity index (Ip = Mw/Mn) and radius of gyration Rg

Communication culture of medical institutions and patients as the factor of maintenance of efficiency of medical aid

Article is devoted communication culture medical establishments and patients as to the factor of maintenance of efficiency of medical aid. Authors on the basis of studying of communication culture of medical institutions and patients a different profile conclusions about necessity of management of this phenomenon on the basis of the complex approach become

Faster homomorphic encryption is not enough: improved heuristic for multiplicative depth minimization of Boolean circuits

International audienceIn somewhat homomorphic encryption schemes (e.g. B/FV, BGV) the size of ciphertexts and the execution performance of homomorphic operations depends heavily on the multiplicative depth. The multiplicative depth is the maximal number of consecutive multiplications for which an homomorphic encryption scheme was parameterized. In this work we propose an improved multiplicative depth minimization heuristic. In particular, a new circuit rewriting operator is introduced, the so called cone rewrite operator. The results we obtain using the new method are relevant in terms of accuracy and performance. Smaller multiplicative depths for a benchmark of Boolean circuits are obtained when compared to a previous work found in the literature. In average, the multiplicative depth is highly improved and the new heuristic execution time is significantly lower. The proposed rewrite operator and heuristic are not limited to Boolean circuits, but can also be used for arithmetic circuits