How to Incentivize Data-Driven Collaboration Among Competing Parties

The availability of vast amounts of data is changing how we can make medical discoveries, predict global market trends, save energy, and develop educational strategies. In some settings such as Genome Wide Association Studies or deep learning, sheer size of data seems critical. When data is held distributedly by many parties, they must share it to reap its full benefits. One obstacle to this revolution is the lack of willingness of different parties to share data, due to reasons such as loss of privacy or competitive edge. Cryptographic works address privacy aspects, but shed no light on individual parties' losses/gains when access to data carries tangible rewards. Even if it is clear that better overall conclusions can be drawn from collaboration, are individual collaborators better off by collaborating? Addressing this question is the topic of this paper. * We formalize a model of n-party collaboration for computing functions over private inputs in which participants receive their outputs in sequence, and the order depends on their private inputs. Each output "improves" on preceding outputs according to a score function. * We say a mechanism for collaboration achieves collaborative equilibrium if it ensures higher reward for all participants when collaborating (rather than working alone). We show that in general, computing a collaborative equilibrium is NP-complete, yet we design efficient algorithms to compute it in a range of natural model settings. Our collaboration mechanisms are in the standard model, and thus require a central trusted party; however, we show this assumption is unnecessary under standard cryptographic assumptions. We show how to implement the mechanisms in a decentralized way with new extensions of secure multiparty computation that impose order/timing constraints on output delivery to different players, as well as privacy and correctness

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

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

Web-based multi-party computation with application to anonymous aggregate compensation analytics

We describe the definition, design, implementation, and deployment of a multi-party computation protocol and supporting web-based infrastructure. The protocol and infrastructure constitute a software application that allows groups of cooperating parties, such as companies or other organizations, to collect aggregate data for statistical analysis without revealing the data of individual participants. The application was developed specifically to support a Boston Women's Workforce Council (BWWC) study of the gender wage gap among employers within the Greater Boston Area. The application was deployed successfully to collect aggregate statistical data pertaining to compensation levels across genders and demographics at a number of participating organizations.We would like to acknowledge all the members of the Boston Women's Workforce Council (BWWC), and to thank in particular Christina M. Knowles and Katie A. Johnston, who led the effort to organize participants and deploy the protocol as part of the 100% Talent: The Boston Women's Compact effort [1, 2]. We would also like to acknowledge the Boston University Initiative on Cities, and in particular Executive Director Katherine Lusk, who brought this potential application of secure multi-party computation to our attention. Both the BWWC and the Initiative on Cities contributed funding to complete this work. We would also like to acknowledge the Hariri Institute at Boston University for contributing research and software development resources. Support was also provided in part by Smart-city Cloud-based Open Platform and Ecosystem (SCOPE), an NSF Division of Industrial Innovation and Partnerships PFI:BIC project under award #1430145, and by Modular Approach to Cloud Security (MACS), an NSF CISE CNS SaTC Frontier project under award #1414119

A System for Distributed Mechanisms: Design, Implementation and Applications

We describe here a structured system for distributed mechanism design appropriate for both Intranet and Internet applications. In our approach the players dynamically form a network in which they know neither their neighbours nor the size of the network and interact to jointly take decisions. The only assumption concerning the underlying communication layer is that for each pair of processes there is a path of neighbours connecting them. This allows us to deal with arbitrary network topologies. We also discuss the implementation of this system which consists of a sequence of layers. The lower layers deal with the operations that implement the basic primitives of distributed computing, namely low level communication and distributed termination, while the upper layers use these primitives to implement high level communication among players, including broadcasting and multicasting, and distributed decision making. This yields a highly flexible distributed system whose specific applications are realized as instances of its top layer. This design is implemented in Java. The system supports at various levels fault-tolerance and includes a provision for distributed policing the purpose of which is to exclude `dishonest' players. Also, it can be used for repeated creation of dynamically formed networks of players interested in a joint decision making implemented by means of a tax-based mechanism. We illustrate its flexibility by discussing a number of implemented examples.Comment: 36 pages; revised and expanded versio

Cryptographic Randomized Response Techniques

We develop cryptographically secure techniques to guarantee unconditional privacy for respondents to polls. Our constructions are efficient and practical, and are shown not to allow cheating respondents to affect the ``tally'' by more than their own vote -- which will be given the exact same weight as that of other respondents. We demonstrate solutions to this problem based on both traditional cryptographic techniques and quantum cryptography.Comment: 21 page