A Rational Threshold Signature Model and Protocol Based on Different Permissions

This paper develops a novel model and protocol used in some specific scenarios, in which the participants of multiple groups with different permissions can finish the signature together. We apply the secret sharing scheme based on difference equation to the private key distribution phase and secret reconstruction phrase of our threshold signature scheme. In addition, our scheme can achieve the signature success because of the punishment strategy of the repeated rational secret sharing. Besides, the bit commitment and verification method used to detect players' cheating behavior acts as a contributing factor to prevent the internal fraud. Using bit commitments, verifiable parameters, and time sequences, this paper constructs a dynamic game model, which has the features of threshold signature management with different permissions, cheat proof, and forward security.Mathematics, AppliedSCI(E)[email protected]

Novel Secret Sharing and Commitment Schemes for Cryptographic Applications

In the second chapter, the notion of a social secret sharing (SSS) scheme is introduced in which shares are allocated based on a player's reputation and the way she interacts with other parties. In other words, this scheme renews shares at each cycle without changing the secret, and it allows the trusted parties to gain more authority. Our motivation is that, in real-world applications, components of a secure scheme have different levels of importance (i.e., the number of shares a player has) and reputation (i.e., cooperation with other parties). Therefore, a good construction should balance these two factors accordingly. In the third chapter, a novel socio-rational secret sharing (SRS) scheme is introduced in which rational foresighted players have long-term interactions in a social context, i.e., players run secret sharing while founding and sustaining a public trust network. To motivate this, consider a repeated secret sharing game such as sealed-bid auctions. If we assume each party has a reputation value, we can then penalize (or reward) the players who are selfish (or unselfish) from game to game. This social reinforcement stimulates the players to be cooperative in the secret recovery phase. Unlike the existing protocols in the literature, the proposed solution is stable and it only has a single reconstruction round. In the fourth chapter, a comprehensive analysis of the existing dynamic secret sharing (DSS) schemes is first provided. In a threshold scheme, the sensitivity of the secret and the number of players may fluctuate due to various reasons. Moreover, a common problem with almost all secret sharing schemes is that they are ``one-time'', meaning that the secret and shares are known to everyone after secret recovery. We therefore provide new techniques where the threshold and/or the secret can be changed multiple times to arbitrary values after the initialization. In addition, we introduce a new application of dynamic threshold schemes, named sequential secret sharing (SQS), in which several secrets with increasing thresholds are shared among the players who have different levels of authority. In the fifth chapter, a cryptographic primitive, named multicomponent commitment scheme (MCS) is proposed where we have multiple committers and verifiers. This new scheme is used to construct different sealed-bid auction protocols (SAP) where the auction outcomes are defined without revealing the losing bids. The main reason for constructing secure auctions is the fact that the values of the losing bids can be exploited in future auctions and negotiations if they are not kept private. In our auctioneer-free protocols, bidders first commit to their bids before the auction starts. They then apply a decreasing price mechanism to define the winner and selling price in an unconditionally secure setting

Rational Trust Modeling

Trust models are widely used in various computer science disciplines. The main purpose of a trust model is to continuously measure trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of "rational trust modeling" is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is where the novelty of our approach comes from. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer's perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivise trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict behavior of the players in subsequent steps by game theoretical analyses

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

Computer Science and Game Theory: A Brief Survey

There has been a remarkable increase in work at the interface of computer science and game theory in the past decade. In this article I survey some of the main themes of work in the area, with a focus on the work in computer science. Given the length constraints, I make no attempt at being comprehensive, especially since other surveys are also available, and a comprehensive survey book will appear shortly.Comment: To appear; Palgrave Dictionary of Economic

Resource-Efficient and Robust Distributed Computing

There has been a tremendous growth in the size of distributed systems in the past three decades. Today, distributed systems, such as the Internet, have become so large that they require highly scalable algorithms; algorithms that have asymptotically-small communication, computation, and latency costs with respect to the network size. Moreover, systems with thousands or even millions of parties distributed throughout the world is likely in danger of faults from untrusted parties. In this dissertation, we study scalable and secure distributed algorithms that can tolerate faults from untrusted parties. Throughout this work, we balance two important and often conflicting characteristics of distributed protocols: security and efficiency. Our first result is a protocol that solves the MPC problem in polylogarithmic communication and computation cost and is secure against an adversary than can corrupt a third of the parties. We adapted our synchronous MPC protocol to the asynchronous setting when the fraction of the corrupted parties are less than 1/8. Next, we presented a scalable protocol that solves the secret sharing problem between rational parties in polylogarithmic communication and computation cost. Furthermore, we presented a protocol that can solve the interactive communication problem over a noisy channel when the noise rate in unknown. In this problem, we have focused on the cost of the protocol in the resource-competitive analysis model. Unlike classic models, resource-competitive models consider the cost that the adversary must pay to succeed in corrupting the protocol

Dynamic joint investments in supply chains under information asymmetry

Supply chain management involves the selection, coordination and motivation of independently operated suppliers. However the central planner's perspective in operations management translates poorly to vertically separated chains, where suppliers may have rational myopic reasons to object to full in- formation sharing and centralized decision rights. Particular problems occur when a downstream coordinator demands relation-specific investments (equipment, cost improvements in processes, adaptation of components to downstream processes, allocation of future capacity etc) from upstream suppliers without being able to commit to long-term contracts. In practice and theory, this leads of- ten to a phenomenon of either underinvestment in the chain or costly vertical integration to solve the commitment problem. A two-stage supply chain under stochastic demand and information asymmetry is modelled. A repeated investment-production game with coordinator commitment in supplier's investment addresses the information sharing and asset- specific investment problem. We provide a mitigation of the hold-up problem on the investment cost observed by the supplier and an instrument for truthful revelation of private information by using an investment sharing device. We show that there is an interior solution for the investment sharing parameter and discuss some extensions to the work.supply chain management, investment, information