Two Party Distribution Testing: Communication and Security

Alexandr Andoni, Tal Malkin, Negev Shekel Nosatzki, Michael Wagner
2019 International Colloquium on Automata, Languages and Programming  
We study the problem of discrete distribution testing in the two-party setting. For example, in the standard closeness testing problem, Alice and Bob each have t samples from, respectively, distributions a and b over [n], and they need to test whether a = b or a, b are -far (in the 1 distance). This is in contrast to the well-studied one-party case, where the tester has unrestricted access to samples of both distributions. Despite being a natural constraint in applications, the two-party
more » ... has previously evaded attention. We address two fundamental aspects of the two-party setting: 1) what is the communication complexity, and 2) can it be accomplished securely, without Alice and Bob learning extra information about each other's input. Besides closeness testing, we also study the independence testing problem, where Alice and Bob have t samples from distributions a and b respectively, which may be correlated; the question is whether a, b are independent or -far from being independent. Our contribution is three-fold: 1) We show how to gain communication efficiency given more samples, beyond the information-theoretic bound on t. The gain is polynomially better than what one would obtain via adapting one-party algorithms. 2) We prove tightness of our trade-off for the closeness testing, as well as that the independence testing requires tight Ω( √ m) communication for unbounded number of samples. These lower bounds are of independent interest as, to the best of our knowledge, these are the first 2-party communication lower bounds for testing problems, where the inputs are a set of i.i.d. samples. 3) We define the concept of secure distribution testing, and provide secure versions of the above protocols with an overhead that is only polynomial in the security parameter. ACM Subject Classification Mathematics of computing → Hypothesis testing and confidence interval computation
doi:10.4230/lipics.icalp.2019.15 dblp:conf/icalp/AndoniMN19 fatcat:g5zqpkzrjvfxliqcgjxkbqjy6e