Evidence-Based Trust Mechanism Using Clustering Algorithms for Distributed Storage Systems (Short Paper)

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS^3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.

[1]  Douglas R. Stinson,et al.  Social secret sharing in cloud computing using a new trust function , 2012, 2012 Tenth Annual International Conference on Privacy, Security and Trust.

[2]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[3]  S. Buchegger,et al.  A Robust Reputation System for Peer-to-Peer and Mobile Ad-hoc Networks , 2004 .

[4]  Andrew W. Moore,et al.  The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data , 2000, UAI.

[5]  Ke Wang,et al.  Bias and controversy: beyond the statistical deviation , 2006, KDD '06.

[6]  Sebastian Ries,et al.  Extending Bayesian trust models regarding context-dependence and user friendly representation , 2009, SAC '09.

[7]  Glynn Winskel,et al.  Petri Nets, Event Structures and Domains, Part I , 1981, Theor. Comput. Sci..

[8]  Vladimiro Sassone,et al.  A Bayesian Model for Event-based Trust , 2022 .

[9]  Lucy Y. Pao,et al.  Variance estimation and ranking of Gaussian mixture distributions in target tracking applications , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[10]  Audun Jøsang,et al.  AIS Electronic Library (AISeL) , 2017 .

[11]  Yuval Ishai,et al.  The round complexity of verifiable secret sharing and secure multicast , 2001, STOC '01.

[12]  V. Ramasubramanian,et al.  A generalized optimization of the K-d tree for fast nearest-neighbour search , 1989, Fourth IEEE Region 10 International Conference TENCON.

[13]  Douglas R. Stinson,et al.  Unconditionally secure social secret sharing scheme , 2010, IET Inf. Secur..

[14]  Svein J. Knapskog,et al.  Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments , 2009, IFIPTM.

[15]  Timothy Lethbridge,et al.  A New Approach for the Trust Calculation in Social Networks , 2006, ICE-B.

[16]  Denise Demirel,et al.  Dynamic and Verifiable Hierarchical Secret Sharing , 2016, ICITS.

[17]  Daniel Slamanig,et al.  PRISMACLOUD - Privacy and Security Maintaining Services in the Cloud , 2016, ERCIM News.

[18]  Munindar P. Singh,et al.  Trustworthy Service Selection and Composition , 2011, TAAS.

[19]  W. M. Bolstad Introduction to Bayesian Statistics , 2004 .

[20]  Daniel Slamanig,et al.  ARCHISTAR: Towards Secure and Robust Cloud Based Data Sharing , 2015, 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom).

[21]  Denise Demirel,et al.  AS3: Adaptive social secret sharing for distributed storage systems , 2016, 2016 14th Annual Conference on Privacy, Security and Trust (PST).

[22]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[23]  Michael I. Jordan Graphical Models , 2003 .

[24]  Huan Liu,et al.  eTrust: understanding trust evolution in an online world , 2012, KDD.

[25]  Xin Liu,et al.  A GENERIC TRUST FRAMEWORK FOR LARGE‐SCALE OPEN SYSTEMS USING MACHINE LEARNING , 2011, Comput. Intell..

[26]  Ee-Peng Lim,et al.  StereoTrust: a group based personalized trust model , 2009, CIKM.

[27]  Douglas R. Stinson,et al.  Brief announcement: secret sharing based on the social behaviors of players , 2010, PODC '10.