Improved Signature Schemes for Secure Multi-Party Computation with Certified Inputs

The motivation for this work comes from the need to strengthen security of secure multi-party protocols with the ability to guarantee that the participants provide their truthful inputs in the computation. This is outside the traditional security models even in the presence of malicious participants, but input manipulation can often lead to privacy and result correctness violations. Thus, in this work we treat the problem of combining secure multi-party computation (SMC) techniques based on secret sharing with signatures to enforce input correctness in the form of certification. We modify two currently available signature schemes to achieve private verification and efficiency of batch verification and show how to integrate them with two prominent SMC protocols

A privacy-preserving fuzzy interest matching protocol for friends finding in social networks

Nowadays, it is very popular to make friends, share photographs, and exchange news throughout social networks. Social networks widely expand the area of people’s social connections and make communication much smoother than ever before. In a social network, there are many social groups established based on common interests among persons, such as learning group, family group, and reading group. People often describe their profiles when registering as a user in a social network. Then social networks can organize these users into groups of friends according to their profiles. However, an important issue must be considered, namely many users’ sensitive profiles could have been leaked out during this process. Therefore, it is reasonable to design a privacy-preserving friends-finding protocol in social network. Toward this goal, we design a fuzzy interest matching protocol based on private set intersection. Concretely, two candidate users can first organize their profiles into sets, then use Bloom filters to generate new data structures, and finally find the intersection sets to decide whether being friends or not in the social network. The protocol is shown to be secure in the malicious model and can be useful for practical purposes.Peer ReviewedPostprint (author's final draft

Revocation in Publicly Verifiable Outsourced Computation

The combination of software-as-a-service and the increasing use of mobile devices gives rise to a considerable difference in computational power between servers and clients. Thus, there is a desire for clients to outsource the evaluation of complex functions to an external server. Servers providing such a service may be rewarded per computation, and as such have an incentive to cheat by returning garbage rather than devoting resources and time to compute a valid result. In this work, we introduce the notion of Revocable Publicly Verifiable Computation (RPVC), where a cheating server is revoked and may not perform future computations (thus incurring a financial penalty). We introduce a Key Distribution Center (KDC) to efficiently handle the generation and distribution of the keys required to support RPVC. The KDC is an authority over entities in the system and enables revocation. We also introduce a notion of blind verification such that results are verifiable (and hence servers can be rewarded or punished) without learning the value. We present a rigorous definitional framework, define a number of new security models and present a construction of such a scheme built upon Key-Policy Attribute-based Encryption.