TrustChain: A Sybil-resistant scalable blockchain

Abstract TrustChain is capable of creating trusted transactions among strangers without central control. This enables new areas of blockchain use with a focus on building trust between individuals. Our innovative approach offers scalability, openness and Sybil-resistance while replacing proof-of-work with a mechanism to establish the validity and integrity of transactions. TrustChain is a permission-less tamper-proof data structure for storing transaction records of agents. We create an immutable chain of temporally ordered interactions for each agent. It is inherently parallel and every agent creates his own genesis block. TrustChain includes a novel Sybil-resistant algorithm named NetFlow to determine trustworthiness of agents in an online community. NetFlow ensures that agents who take resources from the community also contribute back. We demonstrate that irrefutable historical transaction records offer security and seamless scalability, without requiring global consensus. Experimentation shows that the transaction throughput of TrustChain surpasses that of traditional blockchain architectures like Bitcoin. We show by using extracted data from a live network that TrustChain has sufficient informativeness to identify freeriders, leading to refusal of service.

[1]  Johan Pouwelse,et al.  Targeted and scalable information dissemination in a distributed reputation mechanism , 2012, STC '12.

[2]  P. Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2006 .

[3]  Yoad Lewenberg,et al.  Inclusive Block Chain Protocols , 2015, Financial Cryptography.

[4]  Krishna P. Gummadi,et al.  An analysis of social network-based Sybil defenses , 2010, SIGCOMM 2010.

[5]  Lakshminarayanan Subramanian,et al.  Sybil-Resilient Online Content Voting , 2009, NSDI.

[6]  Sven Seuken,et al.  Sybil-proof accounting mechanisms with transitive trust , 2014, AAMAS.

[7]  James B. Orlin,et al.  Max flows in O(nm) time, or better , 2013, STOC '13.

[8]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[9]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[10]  Alexandru Iosup,et al.  TRIBLER: a social-based peer-to-peer system: Research Articles , 2008 .

[11]  Paul Resnick,et al.  Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system , 2002, The Economics of the Internet and E-commerce.

[12]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[13]  Rafid Fadul,et al.  The tragedy of the commons revisited. , 2009, The New England journal of medicine.

[14]  Aviv Zohar,et al.  Secure High-Rate Transaction Processing in Bitcoin , 2015, Financial Cryptography.

[15]  Ruihong Huang,et al.  LOBSTER: Limit Order Book Reconstruction System , 2011 .

[16]  Andrew V. Goldberg,et al.  A new approach to the maximum flow problem , 1986, STOC '86.

[17]  Feng Xiao,et al.  SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks , 2010, IEEE/ACM Trans. Netw..

[18]  Richard M. Karp,et al.  Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems , 1972, Combinatorial Optimization.

[19]  Elaine Shi,et al.  On Scaling Decentralized Blockchains - (A Position Paper) , 2016, Financial Cryptography Workshops.

[20]  Emin Gün Sirer,et al.  Bitcoin-NG: A Scalable Blockchain Protocol , 2015, NSDI.

[21]  Robert E. Tarjan,et al.  A faster deterministic maximum flow algorithm , 1992, SODA '92.

[22]  Sven Seuken,et al.  Accounting Mechanisms for Distributed Work Systems , 2010, AAAI.

[23]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[24]  Johan A. Pouwelse,et al.  Trust-based collection of information in distributed reputation networks , 2015, SAC.

[25]  T. C. Hu,et al.  Multi-Terminal Network Flows , 1961 .

[26]  Michael Kaminsky,et al.  SybilGuard: defending against sybil attacks via social networks , 2006, SIGCOMM.

[27]  Jun Wang,et al.  TRIBLER: a social‐based peer‐to‐peer system , 2008, IPTPS.

[28]  John R. Douceur,et al.  The Sybil Attack , 2002, IPTPS.

[29]  George Danezis,et al.  SybilInfer: Detecting Sybil Nodes using Social Networks , 2009, NDSS.

[30]  Improved Accuracy and Coverage in the BarterCast Reputation Mechanism , 2010 .

[31]  P. Otte.  Sybil-resistant trust mechanisms in distributed systems , 2016 .

[32]  Yefim Dinitz,et al.  Dinitz' Algorithm: The Original Version and Even's Version , 2006, Essays in Memory of Shimon Even.

[33]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[34]  Johan A. Pouwelse,et al.  BarterCast: A practical approach to prevent lazy freeriding in P2P networks , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[35]  Idit Keidar,et al.  Distributed computing column 43: using social networks to overcome Sybil attacks , 2011, SIGA.

[36]  Paul Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2002 .

[37]  Johan A. Pouwelse,et al.  Online Video Using BitTorrent and HTML5 Applied to Wikipedia , 2010, 2010 IEEE Tenth International Conference on Peer-to-Peer Computing (P2P).