|Funding for:||UK Students|
|Funding amount:||£17,668 Stipend pa. Tuition fee: £4,596 pa.|
|Placed On:||30th March 2023|
|Closes:||1st May 2023|
The student should have background in Computer Science and knowledge of basic probability theory.
We will consider applications from students wishing to start during the 2022-23 academic year or who wish to begin their studies in autumn 2023.
The position offered is for three and a half years full-time study. The current (2022-23) value of the award is stipend; £17,668 pa; tuition fee: £4,596 pa. Awards are usually incremented on 1 October each following year.
Hypothesis testing on probability distributions has a wide range of applications, including Statistical Learning Theory and Cryptography. The problem asks to determine, with some “confidence”, whether an unknown probability distribution D over a domain S satisfies certain property (null hypothesis) or it is statistically far from any distribution having that property (alternate hypothesis). Traditional statistical tests follow the standard sampling model, where the tester can draw independent samples following D. Efficiency of statistical or algorithmic testers is measured by the sample complexity. For testing whether a distribution is uniform, information-theoretic lower bounds force any algorithm or statistical test to draw at least √|S| samples from D. If S is the set of 256-bit binary strings, the above bound translates to requiring 2^128 many samples! Security of cryptographic constructions, in particular candidate pseudorandom functions and permutations, is crucially reliant on the infeasibility of efficient testing of probability distributions.
The recently introduced framework of Conditional Sampling surprisingly allows efficient testing with sample complexity of O(log |S|) and sometimes even independent of |S|. In this framework, the tester is allowed to draw samples from D conditioned on an arbitrary subset of S. The specific topic of interest is the subcube conditional framework, where the conditioning is done by fixing some bits of the output and studying the residual distribution.
The aim of the project is to develop algorithms in the subcube conditioning framework  and analyse the security of modern cryptosystems in this framework. The existing results in testing with subcube conditional samples only work when the null hypothesis assumes the underlying distribution to satisfy the target property. However, the distributions are often statistically close to one satisfying the property. The first objective of this project would be to develop algorithms to test the closeness of distributions using subcube conditional samples. The second objective of the project would be to develop a new cryptanalysis technique using these algorithms and analyse the security of popular cryptographic constructions. The project will be supervised by Dr Rishiraj Bhattacharyya (firstname.lastname@example.org).
 Rishiraj Bhattacharyya and Sourav Chakraborty. Property testing of joint distributions using
conditional samples. ACM Trans. Comput. Theory, 10(4):16:1–16:20, 2018. url: https://rishirajb.github.io/pubs/cond.pdf
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