Learner-Private Convex Optimization

TL;DR

This paper explores how to obfuscate queries in convex optimization to protect learner privacy against eavesdroppers, analyzing different formulations and providing bounds on query complexity overhead.

Contribution

It introduces a novel framework for learner privacy in convex optimization with full-gradient feedback, analyzing Bayesian and minimax formulations and deriving complexity bounds.

Findings

Query complexity overhead is additive in L for minimax formulation.

Query complexity overhead is multiplicative in L for Bayesian formulation.

The approach applies to general convex functions with full-gradient feedback.

Abstract

Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function. It has gained considerable popularity thanks to its scalability in large-scale optimization and machine learning. The repeated interactions, however, expose the learner to privacy risks from eavesdropping adversaries that observe the submitted queries. In this paper, we study how to optimally obfuscate the learner's queries in convex optimization with first-order feedback, so that their learned optimal value is provably difficult to estimate for an eavesdropping adversary. We consider two formulations of learner privacy: a Bayesian formulation in which the convex function is drawn randomly, and a minimax formulation in which the function is fixed and the adversary's probability of error is measured with respect to a minimax criterion. Suppose that the learner wishes to ensure the adversary cannot estimate accurately with probability greater than $1/L$ for some $L>0$. Our main results show that the query complexity overhead is additive in $L$ in the minimax formulation, but multiplicative in $L$ in the Bayesian formulation. Compared to existing learner-private sequential learning models with binary feedback, our results apply to the significantly richer family of general convex functions with full-gradient feedback. Our proofs learn on tools from the theory of Dirichlet processes, as well as a novel strategy designed for measuring information leakage under a full-gradient oracle.