DeepSecure: Scalable Provably-Secure Deep Learning

This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves more than 58-fold higher throughput per sample compared with the best-known prior solution. In addition to our optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of deep learning. Extensive evaluations of various DL applications demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology. This paper also provides mechanisms to securely delegate GC computations to a third party in constrained embedded settings

Session Initiation Protocol Attacks and Challenges

In recent years, Session Initiation Protocol (SIP) has become widely used in current internet protocols. It is a text-based protocol much like Hyper Text Transport Protocol (HTTP) and Simple Mail Transport Protocol (SMTP). SIP is a strong enough signaling protocol on the internet for establishing, maintaining, and terminating session. In this paper the areas of security and attacks in SIP are discussed. We consider attacks from diverse related perspectives. The authentication schemes are compared, the representative existing solutions are highlighted, and several remaining research challenges are identified. Finally, the taxonomy of SIP threat will be presented

PROPYLA: Privacy Preserving Long-Term Secure Storage

An increasing amount of sensitive information today is stored electronically and a substantial part of this information (e.g., health records, tax data, legal documents) must be retained over long time periods (e.g., several decades or even centuries). When sensitive data is stored, then integrity and confidentiality must be protected to ensure reliability and privacy. Commonly used cryptographic schemes, however, are not designed for protecting data over such long time periods. Recently, the first storage architecture combining long-term integrity with long-term confidentiality protection was proposed (AsiaCCS'17). However, the architecture only deals with a simplified storage scenario where parts of the stored data cannot be accessed and verified individually. If this is allowed, however, not only the data content itself, but also the access pattern to the data (i.e., the information which data items are accessed at which times) may be sensitive information. Here we present the first long-term secure storage architecture that provides long-term access pattern hiding security in addition to long-term integrity and long-term confidentiality protection. To achieve this, we combine information-theoretic secret sharing, renewable timestamps, and renewable commitments with an information-theoretic oblivious random access machine. Our performance analysis of the proposed architecture shows that achieving long-term integrity, confidentiality, and access pattern hiding security is feasible.Comment: Few changes have been made compared to proceedings versio