Threshold Fully Homomorphic Encryption (FHE) enables arbitrary computation on encrypted data, while distributing the decryption capability across multiple parties. A primary application of interest is low-communication multi-party computation (MPC), which benefits from a fast and secure threshold FHE decryption protocol.

The security and usability of cryptocurrencies and other blockchain-based applications depend on the secure management of cryptographic keys. However, current approaches for managing these keys often rely on third parties, trusted to be available at a minimum, and even serve as custodians in some solutions, creating single points of failure and limiting the ability of users to fully control their own assets. In this work we first revisit the problem of threshold ECDSA by considering the commonly admissible 'server-aided' model, namely, the presence of a semi-honest and non-colluding service provider. Then, we leverage that model and consider cases where that 'server' is distributed, introducing the novel concept of unstoppable wallets; hence eliminating any single point of failure. Unstoppable wallets are programmable threshold ECDSA wallets that allow users to co-sign transactions with a confidential smart contract, rather than a singular third-party. We construct highly efficient threshold ECDSA protocols that form the basis of unstoppable wallets and prove their security in the server-aided model, achieving the standard notion of fairness and robustness even in case of a dishonest majority among the signers. Our protocols minimize the write-complexity for threshold ECDSA key-generation and signing, while reducing communication and computation overhead.

Distributed point functions (DPF) are increasingly becoming a foundational tool with applications for application-specific and general secure computation. While two-party DPF constructions are readily available for those applications with satisfiable performance, the three-party ones are left behind in both security and efficiency. In this paper we close this gap and propose the first three-party DPF construction that matches the state-of-the-art two-party DPF on all metrics. Namely, it is secure against a malicious adversary corrupting both the dealer and one out of the three evaluators, its function's shares are of the same size and evaluation takes the same time as in the best two-party DPF. Compared to the state-of-the-art three-party DPF, our construction enjoys 40-120× smaller function's share size and shorter evaluation time, for function domains of 216 -240, respectively.

Optimizing the use of available resources is one of the key challenges in activities that consist of interactions with a large number of “target individuals,” with the ultimate goal of “winning” as many of them as possible, such as in marketing, service provision, political campaigns, or homeland security. Typically, the cost of interactions is monotonically increasing such that a method for maximizing the performance of these campaigns iPs required. In this paper, we propose a mathematical model to compute an optimized campaign by automatically determining the number of interacting units and their type, and how they should be allocated to different geographical regions in order to maximize the campaign's performance. We validate our proposed model using real world mobility data.