What Information is Leaked under Concurrent Composition
Vipul Goyal, Divya Gupta, Abhishek Jain
A long series of works have established far reaching impossibility results for concurrently secure computation. On the other hand, some positive results have also been obtained according to various weaker notions of security (such as by using a super-polynomial time simulator). This suggest that somehow, “not all is lost in the concurrent setting.” In this work, we ask what and exactly how much private information can an adversary learn by launching a concurrent attack? Inspired by the recent works on leakage-resilient protocols, we consider a security model where the ideal world adversary (a.k.a simulator) is allowed to query the trusted party for some “leakage ” on the honest party inputs. Intuitively, the amount of leakage required by the simulator upper bounds the security loss in the real world. This model generalizes the multiple ideal query model (MIQ) for concurrent security proposed by Goyal, Jain and Ostrovsky [CRYPTO’10]. Positive Results. As our main result, we show, for the first time, how to compute any efficiently computable functionality in the concurrent setting such that full security is guaranteed for “most ” of the sessions. At the heart of this result is a new precise simulation strategy (first
•Jan 1, 2015•2 citations