Sufficient condition for ephemeral key-leakage resilient tripartite key exchange

17th Australasian Conference on Information Security and Privacy, ACISP 2012; Wollongong, NSW; Australia; 9 July 2012 through 11 July 2012Tripartite (Diffie-Hellman) Key Exchange (3KE), introduced by Joux (ANTS-IV 2000), represents today the only known class of group key exchange protocols, in which computation of unauthenticated session keys requires one round and proceeds with minimal computation and communication overhead. The first one-round authenticated 3KE version that preserved the unique efficiency properties of the original protocol and strengthened its security towards resilience against leakage of ephemeral (session-dependent) secrets was proposed recently by Manulis, Suzuki, and Ustaoglu (ICISC 2009). In this work we explore sufficient conditions for building such protocols. We define a set of admissible polynomials and show how their construction generically implies 3KE protocols with the desired security and efficiency properties. Our result generalizes the previous 3KE protocol and gives rise to many new authenticated constructions, all of which enjoy forward secrecy and resilience to ephemeral key-leakage under the gap Bilinear Diffie-Hellman assumption in the random oracle model. © 2012 Springer-Verlag

Scalable Compilers for Group Key Establishment : Two/Three Party to Group

This work presents the first scalable, efficient and generic compilers to construct group key exchange (GKE) protocols from two/three party key exchange (2-KE/3-KE) protocols. We propose three different compilers where the first one is a 2-KE to GKE compiler (2-TGKE) for tree topology, the second one is also for tree topology but from 3-KE to GKE (3-TGKE) and the third one is a compiler that constructs a GKE from 3-KE for circular topology. Our compilers 2-TGKE and 3-TGKE are first of their kind and are efficient due to the underlying tree topology. For the circular topology, we design a compiler called 3-CGKE. 2-TGKE and 3-TGKE compilers require a total of $\mathcal{O}\left(n\lg n \right)$ communication, when compared to the existing compiler for circular topology, where the communication cost is $\mathcal{O}\left(n^2 \right)$. By extending the compilers 2-TGKE and 3-TGKE using the techniques in \cite{DLB07}, scalable compilers for tree based authenticated group key exchange protocols (2-TAGKE/3-TAGKE), which are secure against active adversaries can be constructed. As an added advantage our compilers can be used in a setting where there is asymmetric distribution of computing power. Finally, we present a constant round authenticated group key exchange (2-TAGKE) obtained by applying Diffie-Hellman protocol and the technique in \cite{DLB07} to our compiler 2-TGKE. We prove the security of our compilers in a stronger Real or Random model and do not assume the existence of random oracles

Study of Tools Interoperability

Interoperability of tools usually refers to a combination of methods and techniques that address the problem of making a collection of tools to work together. In this study we survey different notions that are used in this context: interoperability, interaction and integration. We point out relation between these notions, and how it maps to the interoperability problem. We narrow the problem area to the tools development in academia. Tools developed in such environment have a small basis for development, documentation and maintenance. We scrutinise some of the problems and potential solutions related with tools interoperability in such environment. Moreover, we look at two tools developed in the Formal Methods and Tools group1, and analyse the use of different integration techniques

SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation

Nowadays, gathering high-quality training data from multiple data controllers with privacy preservation is a key challenge to train high-quality machine learning models. The potential solutions could dramatically break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academia researchers and industrial vendors are recently strongly motivated to propose two main-stream folders of solutions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short). These two solutions have their advantages and limitations when we evaluate them from privacy preservation, ways of communication, communication overhead, format of data, the accuracy of trained models, and application scenarios. Motivated to demonstrate the research progress and discuss the insights on the future directions, we thoroughly investigate these protocols and frameworks of both MPL and FL. At first, we define the problem of training machine learning models over multiple data sources with privacy-preserving (TMMPP for short). Then, we compare the recent studies of TMMPP from the aspects of the technical routes, parties supported, data partitioning, threat model, and supported machine learning models, to show the advantages and limitations. Next, we introduce the state-of-the-art platforms which support online training over multiple data sources. Finally, we discuss the potential directions to resolve the problem of TMMPP.Comment: 17 pages, 4 figure