The Computational Power of Optimization in Online Learning

We consider the fundamental problem of prediction with expert advice where the experts are "optimizable": there is a black-box optimization oracle that can be used to compute, in constant time, the leading expert in retrospect at any point in time. In this setting, we give a novel online algorithm that attains vanishing regret with respect to $N$ experts in total $\widetilde{O}(\sqrt{N})$ computation time. We also give a lower bound showing that this running time cannot be improved (up to log factors) in the oracle model, thereby exhibiting a quadratic speedup as compared to the standard, oracle-free setting where the required time for vanishing regret is $\widetilde{\Theta}(N)$. These results demonstrate an exponential gap between the power of optimization in online learning and its power in statistical learning: in the latter, an optimization oracle---i.e., an efficient empirical risk minimizer---allows to learn a finite hypothesis class of size $N$ in time $O(\log{N})$. We also study the implications of our results to learning in repeated zero-sum games, in a setting where the players have access to oracles that compute, in constant time, their best-response to any mixed strategy of their opponent. We show that the runtime required for approximating the minimax value of the game in this setting is $\widetilde{\Theta}(\sqrt{N})$, yielding again a quadratic improvement upon the oracle-free setting, where $\widetilde{\Theta}(N)$ is known to be tight

Public Key Encryption Supporting Plaintext Equality Test and User-Specified Authorization

In this paper we investigate a category of public key encryption schemes which supports plaintext equality test and user-specified authorization. With this new primitive, two users, who possess their own public/private key pairs, can issue token(s) to a proxy to authorize it to perform plaintext equality test from their ciphertexts. We provide a formal formulation for this primitive, and present a construction with provable security in our security model. To mitigate the risks against the semi-trusted proxies, we enhance the proposed cryptosystem by integrating the concept of computational client puzzles. As a showcase, we construct a secure personal health record application based on this primitive