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Given two independent weak random sources X, Y , with the same length ℓ and min-entropies bX , bY whose sum is greater than ℓ + Ω(polylog(ℓ/ε)), we construct a deterministic two-source extractor (aka " blender ") that extracts max(bX , bY) + (bX + bY − ℓ − 4 log(1/ε)) bits which are ε-close to uniform. In contrast, best previously published construction [4]… (More)

We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possible examples. Previous research has suggested that the Random Walk model is more powerful than comparable standard models of learning from independent examples , by exhibiting… (More)

We consider a new model for online secure computation on encrypted inputs in the presence of malicious adversaries. The inputs are independent of the circuit computed in the sense that they can be contributed by separate third parties. The model attempts to emulate as closely as possible the model of " Computing with Encrypted Data " that was put forth in… (More)

Multi-party secure computations are general important procedures to compute any function while keeping the security of private inputs. In this work we ask whether preprocessing can allow low latency (that is, small round) secure multi-party protocols that are universally-composable (UC). In particular, we allow any polynomial time preprocessing as long as… (More)

We give efficient and practical protocols for Privacy Preserving Pattern Classification that allow a client to have his data classified by a server, without revealing information to either party, other than the classification result. We illustrate the advantages of such a framework on several real-world scenarios and give secure protocols for several… (More)

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