Access Control in Social Networks: A reachability-Based Approach

Nowadays, social networks are attracting more and more users. These social network subscribers may share personal and sensitive information with a large number of possibly unknown other users, which is in constant evolution. This raises the need of giving users more control on the distribution of their shared content which can be accessed by a community far wider than they may expect. Our concern is to devise and enforce an appropriate access control model for online social networks that enables users to specify their privacy preferences in an expressive way, and, scales well over small, as well as, large social graphs (i.e., regardless to the size of the social graph). In this paper, we propose an access control model for online social networks based on connection characteristics between users, in an extended sense that includes indirect connections. This model provides a conditional access to shared resources based on reachability constraints, between the owner and the requester of a piece of information. Then, we describe the work that we have done to scale the access control enforcement performances over large social graphs. This paper describes PhD work carried out at Télécom ParisTech under the guidance of Talel Abdessalem

A Hybrid Approach to Logic Evaluation

In this thesis, we contribute the hybrid approach – a means of combining the practical advantages of feature-rich logic evaluation in the cloud, with the performance benefits of hand-written, optimized, efficient native code. In the first part of our hybrid approach, we introduce a cloud-based distribution for logic programs, which may be deployed as a service, in standard cloud environments, across cheap commodity hardware. Modern systems are in the cloud; while distributed logic solvers exist, these systems are highly specialized, requiring expensive, resource intensive hardware infrastructures. Our original technique achieves a fully automatic synthesis of cloud infrastructure for logic programs, and includes a range of practical features not present in existing distributed logic solvers. We show that an implementation of the distribution scales effectively within real-world cloud environments, against a distribution over cores of the same machine. We show that our multi-node distribution may be effectively combined with existing multi-threaded techniques to mitigate the network communication cost incurred by distribution. In the second part of our hybrid approach, we introduce extra-logical algorithms, to achieve performance for logic programs that would not be possible within a bottom-up logic evaluation. Modern systems must deliver high performance on big data; however, even the most powerful logic engines, distributed or otherwise, can be beaten by hand-written code on particular problems. We give a novel implementation of a system for the high-impact problem of sink-reachability, designed such that its algorithms may be used in logic programs. A thorough empirical evaluation, across a range of large-scale, real-world datasets, shows our system outperforms the current state of the art for the sink-reachability problem in all cases. Our hybrid approach addresses the two major deficiencies of modern logic systems, providing a practical means of evaluating logic in distributed cloud-based environments, while offering performance gains for specific high-impact problems that would not be possible using logic programming alone