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When there exists a malicious attacker in the network, we need to consider the possibilities of eavesdropping and the contamination simultaneously. Under an acyclic broadcast network, the optimality of linear codes was shown when Eve is allowed to attack any r edges. The optimality of linear codes is not shown under a different assumption for Eve. As a typical example of an acyclic unicast network, we focus on the one-hop relay network under the single transmission scheme by assuming that Eve attacks only one edge in each level.

We propose a novel hybrid universal network-coding cryptosystem (HUNCC) to obtain secure post-quantum cryptography at high communication rates. The secure network-coding scheme we offer is hybrid in the sense that it combines information-theoretic security with public-key cryptography. In addition, the scheme is general and can be applied to any communication network, and to any public-key cryptosystem.

Quantum private information retrieval (QPIR) is a protocol in which a user retrieves one of multiple classical files by downloading quantum systems from non-communicating $\mathsf {n}$ servers each of which contains a copy of all files, while the identity of the retrieved file is unknown to each server. Symmetric QPIR (QSPIR) is QPIR in which the user only obtains the queried file but no other information of the other files.

The problem of secret-key-based authentication under privacy and storage constraints on the source sequence is considered. Identifier measurement channels during authentication are assumed to be controllable via a cost-constrained action sequence. Inner and outer bounds for the key-leakage-storage-cost regions are derived for a generalization of the classic two-terminal key agreement model.

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.

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the goal is to allow a master to efficiently compute the pairwise products of two batches of massive matrices, by distributing the computation across S servers. Any X colluding servers gain no information about the input, and the master gains no additional information about the input beyond the product. A solution called Generalized Cross Subspace Alignment codes with Noise Alignment (GCSA- NA) is proposed in this work, based on cross-subspace alignment codes.

We consider a decentralized learning setting in which data is distributed over nodes in a graph. The goal is to learn a global model on the distributed data without involving any central entity that needs to be trusted. While gossip-based stochastic gradient descent (SGD) can be used to achieve this learning objective, it incurs high communication and computation costs. To speed up the convergence, we propose instead to study random walk based SGD in which a global model is updated based on a random walk on the graph.

We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy of their data while enabling a central server to perform the tests. Under the notion of local differential privacy, we propose simple, sample-optimal, and communication-efficient protocols for these two questions in the noninteractive setting, where in addition users may or may not share a common random seed.