Â鶹´«Ã½AV

We study a game-theoretic model where a data collector purchases data from users through a payment mechanism. Each user has her personal signal which represents her knowledge about the underlying state the data collector desires to learn. Through social interactions, each user can also learn noisy versions of her friends’ personal signals, which are called ‘group signals’. We develop a Bayesian game theoretic framework to study the impact of social learning on users’ data reporting strategies and devise the payment mechanism for the data collector accordingly.

We introduce fundamental bounds on achievable cumulative rate distribution functions (CRDF) to characterize a sequential encoding process that ensures lossless or lossy reconstruction subject to an average distortion criterion using a non-causal decoder. The CRDF describes the rate resources spent sequentially to compress the sequence. We also include a security constraint that affects the set of achievable CRDF. The information leakage is defined sequentially based on the mutual information between the source and its compressed representation, as it evolves.

Double blind T-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ( θ1, θ2, resp.), to efficiently retrieve a message W(θ1,θ2) labeled by the two indices, from a set of N servers that store all messages W(k1,k2), k1 ∈ {1,2,..., K1}, k2 ∈ {1,2,..., K2}, such that the two users' indices are kept private from any set of up to T1,T2 colluding servers, respectively, as well as from each other.