Multi-party trust computation in decentralized environments in the presence of malicious adversaries

In this paper, we describe a decentralized privacy-preserving protocol for securely casting trust ratings in distributed reputation systems. Our protocol allows n participants to cast their votes in a way that preserves the privacy of individual values against both internal and external attacks. The protocol is coupled with an extensive theoretical analysis in which we formally prove that our protocol is resistant to collusion against as many as n-1 corrupted nodes in both the semi-honest and malicious adversarial models. The behavior of our protocol is tested in a real P2P network by measuring its communication delay and processing overhead. The experimental results uncover the advantages of our protocol over previous works in the area; without sacrificing security, our decentralized protocol is shown to be almost one order of magnitude faster than the previous best protocol for providing anonymous feedback

Edgelet Computing: Pushing Query Processing and Liability at the Extreme Edge of the Network

National audienceWe call edgelet computing the current convergence between Opportunistic Network (OppNet) and Trusted Execution Environment (TEE) at the very edge of the network. We believe that this convergence bears the seeds of a novel and important class of applications leveraging fully decentralized and highly secure computations among data scattered on multiple personal devices. This paper introduces the Edgelet computing paradigm, defines properties that guarantee the safety, liveness and security of executions in this unusual context and proposes alternative strategies satisfying these properties. Preliminary performance evaluations and an ongoing real-case study highlights the practicality of the approach. Finally, the paper draws future research challenges for the database and distributed system community

Edgelet Computing: Pushing Query Processing and Liability at the Extreme Edge of the Network

National audienceWe call edgelet computing the current convergence between Opportunistic Network (OppNet) and Trusted Execution Environment (TEE) at the very edge of the network. We believe that this convergence bears the seeds of a novel and important class of applications leveraging fully decentralized and highly secure computations among data scattered on multiple personal devices. This paper introduces the Edgelet computing paradigm, defines properties that guarantee the safety, liveness and security of executions in this unusual context and proposes alternative strategies satisfying these properties. Preliminary performance evaluations and an ongoing real-case study highlights the practicality of the approach. Finally, the paper draws future research challenges for the database and distributed system community

Privacy Preserving Data Mining For Horizontally Distributed Medical Data Analysis

To build reliable prediction models and identify useful patterns, assembling data sets from databases maintained by different sources such as hospitals becomes increasingly common; however, it might divulge sensitive information about individuals and thus leads to increased concerns about privacy, which in turn prevents different parties from sharing information. Privacy Preserving Distributed Data Mining (PPDDM) provides a means to address this issue without accessing actual data values to avoid the disclosure of information beyond the final result. In recent years, a number of state-of-the-art PPDDM approaches have been developed, most of which are based on Secure Multiparty Computation (SMC). SMC requires expensive communication cost and sophisticated secure computation. Besides, the mining progress is inevitable to slow down due to the increasing volume of the aggregated data. In this work, a new framework named Privacy-Aware Non-linear SVM (PAN-SVM) is proposed to build a PPDDM model from multiple data sources. PAN-SVM employs the Secure Sum Protocol to protect privacy at the bottom layer, and reduces the complex communication and computation via Nystrom matrix approximation and Eigen decomposition methods at the medium layer. The top layer of PAN-SVM speeds up the whole algorithm for large scale datasets. Based on the proposed framework of PAN-SVM, a Privacy Preserving Multi-class Classifier is built, and the experimental results on several benchmark datasets and microarray datasets show its abilities to improve classification accuracy compared with a regular SVM. In addition, two Privacy Preserving Feature Selection methods are also proposed based on PAN-SVM, and tested by using benchmark data and real world data. PAN-SVM does not depend on a trusted third party; all participants collaborate equally. Many experimental results show that PAN-SVM can not only effectively solve the problem of collaborative privacy-preserving data mining by building non-linear classification rules, but also significantly improve the performance of built classifiers

TOWARD LOWER COMMUNICATION IN GARBLED CIRCUIT EVALUATION

Secure Multi-party Computation (SMC) is a classical problem in theoretical security. In a SMC problem, two or more parties must compute correctly a function f on their respective inputs x and y, while preserving the privacy of their inputs and additional security properties. One of the approaches proposed for addressing the SMC problem relies on the design of Garbled Circuit (GC). In Garbled Circuits (GCs), the function to be computed is represented as a Boolean circuit composed of binary gates. The input and output wire of each gate is masked such that the party evaluating the Garbled Boolean Circuits (GBC) cannot gain any information about the inputs or the intermediate results that appear during the function evaluation. The complexity of today's most efficient GC protocol depends linearly on the size of the Boolean circuit representation of the evaluated function. The total cost and run-time interaction between parties increase linearly with the number of gates and can be huge for complex GBCs. Actually, interest has grown in the efficiency of this technique and in its applications to computation outsourcing in untrusted environments. A recent work shows that XOR gates in a Boolean circuit have no cost for the secure computation protocol. Therefore, circuits with a reduced number of non-XOR gates are more convenient and one of the possible ways to reduce the complexity of the computation is to reduce the number of non-XOR gates in the Boolean circuit. Recalling that, the main aim of this work is to reduce the number of non-XOR gates, which directly results in a reduced number of interactions between the parties and transfer complexity at runtime, we present different approaches for reducing the communication cost of Secure Multi-party Computation (SMC) and improving the overall computation time and efficiency of the execution of SMC

Preuve de localisation : calculs multi-parties sécurisés

Aujourd’hui, la grande majorité des gens possède en permanence un téléphone qui leur permet à la fois de se géolocaliser (GPS), mais aussi de rester connectés entre eux (GSM - WIFI). Grâce à ces nouvelles technologies, on pourrait permettre aux individus d’obtenir des preuves de leur localisation, authentifiées par les utilisateurs environnants. Ainsi, un prouveur pourrait affirmer devant un juge qu’il ne se situait pas sur les lieux d’un crime à une date et une heure précise, et que plusieurs témoins ont certifié que cette preuve était correcte. Cependant, cette approche pose un certain nombre de problèmes concernant la vie privée et l’anonymat des divers intervenants. En effet, le prouveur ne souhaite sans doute pas diffuser sa position publiquement à chaque fois qu’il demande d’obtenir une preuve de sa localisation. De même, les témoins préféreraient garder leur identité et leur position secrètes. Par ailleurs, il est important que le juge puisse tout de même récupérer ces informations confidentielles, le juge devant être en mesure de déceler des complicités parmi les témoins. En effet, le juge doit pouvoir détecter si le prouveur essaie de forger de fausses preuves avec l’aide de certains témoins. L’objectif de ce mémoire est donc de concevoir des protocoles de calcul multi-parties permettant à un prouveur d’obtenir des preuves de localisation signées par des témoins, tout en respectant la vie privée des participants et en offrant au seul juge la possibilité de connaître ce qui lui est nécessaire