Raziel: Private and Verifiable Smart Contracts on Blockchains

Raziel combines secure multi-party computation and proof-carrying code to provide privacy, correctness and verifiability guarantees for smart contracts on blockchains. Effectively solving DAO and Gyges attacks, this paper describes an implementation and presents examples to demonstrate its practical viability (e.g., private and verifiable crowdfundings and investment funds). Additionally, we show how to use Zero-Knowledge Proofs of Proofs (i.e., Proof-Carrying Code certificates) to prove the validity of smart contracts to third parties before their execution without revealing anything else. Finally, we show how miners could get rewarded for generating pre-processing data for secure multi-party computation.Comment: Support: cothority/ByzCoin/OmniLedge

Practically Efficient Secure Single-Commodity Multi-Market Auctions

We study the problem of securely building single-commodity multi-markets auction mechanisms. We introduce a novel greedy algorithm and its corresponding privacy preserving implementation using secure multiparty computation. More specifically, we determine the quantity of supply and demand bids maximizing welfare. Each bid is attached to a specific market, but exchanges between different markets are allowed up to some upper limit. The general goal is for the players to bid their intended valuations without concerns about what the other players can learn. This problem is inspired by day-ahead electricity markets where there are substantial transmission capacity between the different markets, but applies to other commodity markets like gas. Furthermore, we provide computational results with a specific C++ implementation of our algorithm and the necessary MPC primitives. We can solve problems of 1945 bids and 4 markets in 1280 seconds when online/offline phases are considered. Finally, we report on possible set-ups, workload distributions and possible trade-offs for real-life applications of our results based on this experimentation and prototyping

Practically efficient secure single-commodity multi-market auctions

We study the problem of securely building single-commodity multi-markets auction mechanisms. We introduce a novel greedy algorithm and its corresponding privacy preserving implementation using secure multi-party computation. More specifically, we determine the quantity of supply and demand bids maximizing welfare. Each bid is attached to a specific market, but exchanges between different markets are allowed up to some upper limit. The general goal is for the players to bid their intended valuations without concerns about what the other players can learn. This problem is inspired by day-ahead electricity markets where there are substantial transmission capacity between the different markets, but applies to other commodity markets like gas. Furthermore, we provide computational results with a specific C++ implementation of our algorithm and the necessary MPC primitives.We can solve problems of 1945 bids and 4 markets in 1280s when online/offline phases are considered. Finally, we report on possible set-ups, workload distributions and possible trade-offs for real-life applications of our results based on this experimentation and prototyping

Survey on Fully Homomorphic Encryption, Theory, and Applications

Data privacy concerns are increasing significantly in the context of Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next generation networks. Homomorphic Encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption (FHE). It consequently covers design fundamentals and security properties of FHE and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof