Talek: Private Group Messaging with Hidden Access Patterns

Talek is a private group messaging system that sends messages through potentially untrustworthy servers, while hiding both data content and the communication patterns among its users. Talek explores a new point in the design space of private messaging; it guarantees access sequence indistinguishability, which is among the strongest guarantees in the space, while assuming an anytrust threat model, which is only slightly weaker than the strongest threat model currently found in related work. Our results suggest that this is a pragmatic point in the design space, since it supports strong privacy and good performance: we demonstrate a 3-server Talek cluster that achieves throughput of 9,433 messages/second for 32,000 active users with 1.7-second end-to-end latency. To achieve its security goals without coordination between clients, Talek relies on information-theoretic private information retrieval. To achieve good performance and minimize server-side storage, Talek intro- duces new techniques and optimizations that may be of independent interest, e.g., a novel use of blocked cuckoo hashing and support for private notifications. The latter provide a private, efficient mechanism for users to learn, without polling, which logs have new messages

Tandem: Securing Keys by Using a Central Server While Preserving Privacy

Users’ devices, e.g., smartphones or laptops, are typically incapable of securely storing and processing cryptographic keys.We present Tandem, a novel set of protocols for securing cryptographic keys with support from a central server. Tandem uses one-time-use key-share tokens to preserve users’ privacy with respect to a malicious central server. Additionally, Tandem enables users to block their keys if they lose their device, and it enables the server to limit how often an adversary can use an unblocked key. We prove Tandem’s security and privacy properties, apply Tandem to attributebased credentials, and implement a Tandem proof of concept to show that it causes little overhead

Out-of-sample kernel extensions for nonparametric dimensionality reduction

Gisbrecht A, Lueks W, Mokbel B, Hammer B. Out-of-sample kernel extensions for nonparametric dimensionality reduction. In: ESANN 2012. 2012: 531-536

Private Set Matching Protocols

We introduce Private Set Matching (PSM) problems, in which a client aims to determine whether a collection of sets owned by a server matches her interest. Existing privacy-preserving cryptographic primitives cannot solve PSM problems efficiently without harming privacy. We propose a new modular framework that enables designers to build privacy-friendly PSM systems that output one bit: whether a server set or collection of server sets matches the client's set, while guaranteeing privacy of client and server sets. The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server sets and their total size. We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PSM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. Our evaluation shows that our privacy gain comes at a reasonable communication and computation cost