Non-interactive (t, n)-Incidence Counting from Differentially Private Indicator Vectors
Mohammad Alaggan, Mathieu Cunche, Marine Minier · 2017
We present a novel non-interactive (t,n)-incidence count estimation for indicator vectors ensuring Differential Privacy. Given one or two differentially private indicator vectors, estimating the distinct count of elements in each and their intersection cardinality (equivalently, their inner product) have been studied in the literature, along with other extensions for estimating the cardinality set intersection in case the elements are hashed prior to insertion. The core contribution behind all these studies was to address the problem of estimating the Hamming weight (the number of bits set to one) of a bit vector from its differentially private version, and in the case of inner product and set intersection, estimating the number of positions which are jointly set to one in both bit vectors.