SPEAKER: Adriana Lopez-Alt NYU Adriana will begin by briefly describing the result of Dodis et al. (DOPS'04 - FOCS 2004), who showed that Santha-Vazirani (SV) sources (where each next bit has fresh entropy, but is allowed to have a small bias \gamma< 1 possibly depending on prior bits) are not sufficient for building computationally secure encryption (even of a single bit), and, if fact, essentially any other cryptographic task involving "privacy" (e.g., commitment, zero-knowledge, secret sharing, etc.). She will then describe recent results from her paper in CRYPTO 2012. ABSTRACT: In this work we revisit the question of basing cryptography on imperfect randomness. Bosley and Dodis (TCC'07) showed that if a source of randomness R is "good enough" to generate a secret key capable of encrypting k bits, then one can deterministically extract nearly k almost uniform bits from R, suggesting that traditional privacy notions (namely, indistinguishability of encryption) requires an extractable source of randomness. Other, even stronger impossibility results are known for achieving privacy under specific "non-extractable" sources of randomness, such as the \gamma-SV source. We ask whether similar negative results also hold for a more recent notion of privacy called differential privacy (Dwork et al., TCC'06), concentrating, in particular, on achieving differential privacy with the Santha-Vazirani source. We show that the answer is no. Specifically, we give a differentially private mechanism for approximating arbitrary "low sensitivity" functions that works even with randomness coming from a \gamma-Santha-Vazirani source, for any \gamma<1. This provides a somewhat surprising "separation" between traditional privacy and differential privacy with respect to imperfect randomness. Interestingly, the design of our mechanism is quite different from the traditional "additive-noise" mechanisms (e.g., Laplace mechanism) successfully utilized to achieve differential privacy with perfect randomness. Indeed, we show that any (accurate and private) "SV-robust" mechanism for our problem requires a demanding property called consistent sampling, which is strictly stronger than differential privacy, and cannot be satisfied by any additive-noise mechanism. Joint work with Yevgeniy Dodis, Ilya Mironov, and Salil Vadhan. Full version can be found at: http://eprint.iacr.org/2012/435