The Sparse Vector Technique
نویسنده
چکیده
We’re going to take a short break from the problem of private query release to develop another fundamental technique in differential privacy. Don’t worry – we’ll soon use this to improve our query release algorithms. Suppose that a data analyst wants to know the answers to k adaptively chosen, low-sensitivity queries on a private database. At the moment, the only way we know how to handle adaptively chosen queries is by using the Laplace mechanism, and paying a cost in our privacy parameter proportional to k (or √ k for ( , δ)-privacy). But what if the data analyst has reason to believe that only a very small number of his queries (say c of them) will take value above a certain threshold T? Moreover, what if he only cares about the values of those queries that actually evaluate above the threshold? If he knew which queries those were, he could ask only the c relevant queries, and pay a privacy cost proportional only to c. The problem is he doesn’t... In this lecture, we’ll show an algorithm for answering any sequence of k adaptively chosen low sensitivity queries, while paying a privacy cost proportional only to those queries that are above a given threshold T .
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