Private Sequential Learning
This addresses privacy concerns in sequential data analysis, but it is incremental as it builds on existing private learning models.
The paper tackles the problem of balancing privacy and query efficiency in sequential learning, where a learner queries a database to estimate a value while an adversary observes queries, and provides tight bounds on query complexity with optimal strategies up to an additive constant.
We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, $v^*$, by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes the learner's queries, though not the responses, and tries to infer from them the value of $v^*$. The objective of the learner is to obtain an accurate estimate of $v^*$ using only a small number of queries, while simultaneously protecting her privacy by making $v^*$ provably difficult to learn for the adversary. Our main results provide tight upper and lower bounds on the learner's query complexity as a function of desired levels of privacy and estimation accuracy. We also construct explicit query strategies whose complexity is optimal up to an additive constant.