Jiapeng Zhang

Improved Noisy Population Recovery, and Reverse Bonami-Beckner Inequality for Sparse Functions

The noisy population recovery problem is a basic statistical inference problem. Given an unknown distribution in {0,1}n with support of size k, and given access only to noisy samples from it, where each bit is flipped independently with probability (1-μ)/2, estimate the original probability up to an additive error of ε. We give an algorithm which solves this problem in time polynomial in (klog log k, n, 1/ε). This improves on the previous algorithm of Wigderson and Yehudayoff [FOCS 2012] which solves the problem in time polynomial in (klog k, n, 1/ε). Our main technical contribution, which facilitates the algorithm, is a new reverse Bonami-Beckner inequality for the L1 norm of sparse functions.

Active Classification with Comparison Queries

We study an extension of active learning in which the learning algorithm may ask the annotator to compare the distances of two examples from the boundary of their label-class. For example, in a recommendation system application (say for restaurants), the annotator may be asked whether she liked or disliked a specific restaurant (a label query); or which one of two restaurants did she like more (a comparison query).We focus on the class of half spaces, and show that under natural assumptions, such as large margin or bounded bit-description of the input examples, it is possible to reveal all the labels of a sample of size n using approximately O(log n) queries. This implies an exponential improvement over classical active learning, where only label queries are allowed. We complement these results by showing that if any of these assumptions is removed then, in the worst case, Ω(n) queries are required.Our results follow from a new general framework of active learning with additional queries. We identify a combinatorial dimension, called the inference dimension, that captures the query complexity when each additional query is determined by O(1) examples (such as comparison queries, each of which is determined by the two compared examples). Our results for half spaces follow by bounding the inference dimension in the cases discussed above.

Barriers to Black-Box Constructions of Traitor Tracing Systems

Reducibility between different cryptographic primitives is a fundamental problem in modern cryptography. As one of the primitives, traitor tracing systems help content distributors recover the identities of users that collaborated in the pirate construction by tracing pirate decryption boxes. We present the first negative result on designing efficient traitor tracing systems via black-box constructions from symmetric cryptographic primitives, e.g. one-way functions. More specifically, we show that there is no secure traitor tracing scheme in the random oracle model, such that \(\ell _k\cdot \ell _c^2<\widetilde{\varOmega }(n)\), where \(\ell _k\) is the length of user key, \(\ell _c\) is the length of ciphertext and n is the number of users, under the assumption that the scheme does not access the oracle to generate private user keys. To our best knowledge, all the existing cryptographic schemes (not limited to traitor tracing systems) via black-box constructions from one-way functions satisfy this assumption. Thus, our negative results indicate that most of the standard black-box reductions in cryptography cannot help construct a more efficient traitor tracing system.

On the Impossibility of Entropy Reversal, and Its Application to Zero-Knowledge Proofs

Zero knowledge proof systems have been widely studied in cryptography. In the statistical setting, two classes of proof systems studied are Statistical Zero Knowledge (SZK) and Non-Interactive Statistical Zero Knowledge (NISZK), where the difference is that in NISZK only very limited communication is allowed between the verifier and the prover. It is an open problem whether these two classes are in fact equal. In this paper, we rule out efficient black box reductions between SZK and NISZK.