The problem of generating an RSA composite in a distributed manner without leaking its factorization is particularly challenging and useful in many cryptographic protocols. Our first contribution is the first non-generic fully simulatable protocol for distributively generating an RSA composite with security against malicious behavior. Our second contribution is a complete Paillier (in: EUROCRYPT, pp 223–238, 1999 ) threshold encryption scheme in the two-party setting with security against malicious attacks. We further describe how to extend our protocols to the multiparty setting with dishonest majority. Our RSA key generation protocol is comprised of the following subprotocols: ( i ) a distributed protocol for generation of an RSA composite and ( ii ) a biprimality test for verifying the validity of the generated composite. Our Paillier threshold encryption scheme uses the RSA composite for the public key and is comprised of the following subprotocols: ( i ) a distributed generation of the corresponding secret key shares and ( ii ) a distributed decryption protocol for decrypting according to Paillier.

Clustering is a very important tool in data mining and is widely used in on-line services for medical, financial and social environments. The main goal in clustering is to create sets of similar objects in a data set. The data set to be used for clustering can be owned by a single entity, or in some cases, information from different databases is pooled to enrich the data so that the merged database can improve the clustering effort. However, in either case, the content of the database may be privacy sensitive and/or commercially valuable such that the owners may not want to share their data with any other entity, including the service provider. Such privacy concerns lead to trust issues between entities, which clearly damages the functioning of the service and even blocks cooperation between entities with similar data sets. To enable joint efforts with private data, we propose a protocol for distributed clustering that limits information leakage to the untrusted service provider that performs the clustering. To achieve this goal, we rely on cryptographic techniques, in particular homomorphic encryption, and further improve the state of the art of processing encrypted data in terms of efficiency by taking the distributed structure of the system into account and improving the efficiency in terms of computation and communication by data packing. While our construction can be easily adjusted to a centralized or a distributed computing model, we rely on a set of particular users that help the service provider with computations. Experimental results clearly indicate that the work we present is an efficient way of deploying a privacy-preserving clustering algorithm in a distributed manner.

Interest in anonymous communication over distributed hash tables (DHTs) has increased in recent years. However, almost all known solutions solely aim at achieving sender or requestor anonymity in DHT queries. In many application scenarios, it is crucial that the queried key remains secret from intermediate peers that (help to) route the queries towards their destinations. In this paper, we satisfy this requirement by presenting an approach for providing privacy for the keys in DHT queries.

Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties, but not the service provider. This creates a serious privacy risk for the users. In this paper, we aim to protect the private data against the service provider while preserving the functionality of the system. We propose encrypting private data and processing them under encryption to generate recommendations. By introducing a semitrusted third party and using data packing, we construct a highly efficient system that does not require the active participation of the user. We also present a comparison protocol, which is the first one to the best of our knowledge, that compares multiple values that are packed in one encryption. Conducted experiments show that this work opens a door to generate private recommendations in a privacy-preserving manner.

This work considers data structures based on multi-party computation (MPC) primitives: structuring secret (e.g. secret shared and potentially unknown) data such that it can both be queried and updated efficiently. Implementing an oblivious RAM (ORAM) using MPC allows any existing data structure to be realized using MPC primitives, however, by focusing on a specific example -- a priority queue -- it is shown that it is possible to achieve much better results than the generic solutions can provide. Moreover, the techniques differ significantly from existing ORAM constructions. Indeed it has recently been shown that any information theoretically secure ORAM with n memory locations requires at least log n random bits per read/write to hide the access pattern. In contrast, the present construction achieves security with a completely deterministic access pattern.