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Eshan Chattopadhyay

Publications and source records attributed to Eshan Chattopadhyay.

20 records · Page 2Linked to original sources

Non-Malleable Extractors and Codes, with their Many Tampered Extensions

Randomness extractors and error correcting codes are fundamental objects in computer science. Recently, there have been several natural generalizations of these objects, in the context and study of tamper resilient cryptography. These are seeded non-malleable extractors, introduced in [DW09]; seedless non-malleable extractors, introduced in [CG14b]; and non-malleable codes, introduced in [DPW10]. However, explicit constructions of non-malleable extractors appear to be hard, and the known constructions are far behind their non-tampered counterparts. In this paper we make progress towards solving the above problems. Our contributions are as follows. (1) We construct an explicit seeded non-malleable extractor for min-entropy $k \geq \log^2 n$. This dramatically improves all previous results and gives a simpler 2-round privacy amplification protocol with optimal entropy loss, matching the best known result in [Li15b]. (2) We construct the first explicit non-malleable two-source extractor for min-entropy $k \geq n-n^{Ω(1)}$, with output size $n^{Ω(1)}$ and error $2^{-n^{Ω(1)}}$. (3) We initiate the study of two natural generalizations of seedless non-malleable extractors and non-malleable codes, where the sources or the codeword may be tampered many times. We construct the first explicit non-malleable two-source extractor with tampering degree $t$ up to $n^{Ω(1)}$, which works for min-entropy $k \geq n-n^{Ω(1)}$, with output size $n^{Ω(1)}$ and error $2^{-n^{Ω(1)}}$. We show that we can efficiently sample uniformly from any pre-image. By the connection in [CG14b], we also obtain the first explicit non-malleable codes with tampering degree $t$ up to $n^{Ω(1)}$, relative rate $n^{Ω(1)}/n$, and error $2^{-n^{Ω(1)}}$.

cs.CR↗

On Low Discrepancy Samplings in Product Spaces of Motion Groups

Deterministically generating near-uniform point samplings of the motion groups like SO(3), SE(3) and their n-wise products SO(3)^n, SE(3)^n is fundamental to numerous applications in computational and data sciences. The natural measure of sampling quality is discrepancy. In this work, our main goal is construct low discrepancy deterministic samplings in product spaces of the motion groups. To this end, we develop a novel strategy (using a two-step discrepancy construction) that leads to an almost exponential improvement in size (from the trivial direct product). To the best of our knowledge, this is the first nontrivial construction for SO(3)^n, SE(3)^n and the hypertorus T^n. We also construct new low discrepancy samplings of S^2 and SO(3). The central component in our construction for SO(3) is an explicit construction of N points in S^2 with discrepancy \tildeØ(1/\sqrt{N}) with respect to convex sets, matching the bound achieved for the special case of spherical caps in \cite{ABD_12}. We also generalize the discrepancy of Cartesian product sets \cite{Chazelle04thediscrepancy} to the discrepancy of local Cartesian product sets. The tools we develop should be useful in generating low discrepancy samplings of other complicated geometric spaces.

cs.CG↗