Search

Collaborative optimization problems can often be modeled as a linear program whose objective function and constraints combine data from several parties. However, important applications of this model (e.g., supply chain planning) involve private data that the parties cannot reveal to each other. Traditional linear programming methods cannot be used in this case. The problem can be solved using cryptographic protocols that compute with private data and preserve data privacy. We present a practical solution using multiparty computation based on secret sharing. The linear programming protocols use a variant of the simplex algorithm and secure computation with fixed-point rational numbers, optimized for this type of application. We present the main protocols as well as performance measurements for an implementation of our solution.

The sustained popularity of the cloud and cloud-related services accelerate the evolution of virtualization-enabling technologies. Modern off-the-shelf computers are already equipped with specialized hardware that enables a hypervisor to manage the simultaneous execution of multiple operating systems. Researchers have proposed security mechanisms that operate within such a hypervisor to protect the \textit{virtualized} operating systems from attacks. These mechanisms improve in security over previous techniques since the defense system is no longer part of an operating system's attack surface. However, due to constant transitions between the hypervisor and the operating systems, these countermeasures typically incur a significant performance overhead. In this paper we present HyperForce, a framework which allows the deployment of security-critical code in a way that significantly outperforms previous \textit{in-hypervisor} systems while maintaining similar guarantees with respect to security and integrity. HyperForce is a hybrid system which combines the performance of an \textit{in-guest} security mechanism with the security of in-hypervisor one. We evaluate our framework by using it to re-implement an invariance-based rootkit detection system and show the performance benefits of a HyperForce-utilizing countermeasure., 12 pages, SEC, Heraklion, 04-06 June 2012, IFIP Advances in Information and Communication Technology 2012

In this paper, we introduce a framework for measuring un-linkability both per subject and for an entire system. The framework enables the evaluator to attach different sensitivities to individual items in the system, and to specify the severity of different types of error that an adversary can make. These parameters, as well as a threshold that defines what constitutes a privacy breach, may be varied for each subject in the system; the framework respects and combines these potentially differing parametrisations. It also makes use of graphs in a way that results in intuitive feedback of different levels of detail.We exhibit the behaviour of our measures in two experimental settings, namely that of adversaries that output randomly chosen partitions, and that of adversaries that launch attacks of different effectiveness.