Data centric trust evaluation and prediction framework for IOT

Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas.

[1]  Adam Wierzbicki,et al.  Enriching Trust Prediction Model in Social Network with User Rating Similarity , 2009, 2009 International Conference on Computational Aspects of Social Networks.

[2]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[3]  Weisong Shi,et al.  Enforcing Cooperative Resource Sharing in Untrusted P2P Computing Environments , 2005, Mob. Networks Appl..

[4]  Richard Y. Wang,et al.  Data Quality Assessment , 2002 .

[5]  Antonio Iera,et al.  A subjective model for trustworthiness evaluation in the social Internet of Things , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[6]  Cecile Paris,et al.  STrust: A Trust Model for Social Networks , 2011, 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications.

[7]  Stephen Marsh,et al.  Formalising Trust as a Computational Concept , 1994 .

[8]  Marcus Kaiser,et al.  How to Measure Data Quality? - A Metric-Based Approach , 2007, ICIS.

[9]  Zhouping Xin,et al.  Step-sizes for the gradient method , 2008 .

[10]  Gyu Myoung Lee,et al.  RpR: A Trust Computation Model for Social Internet of Things , 2016, 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld).

[11]  Julita Vassileva,et al.  Bayesian network-based trust model , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[12]  Rino Falcone,et al.  Principles of trust for MAS: cognitive anatomy, social importance, and quantification , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[13]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[14]  Diane M. Strong,et al.  AIMQ: a methodology for information quality assessment , 2002, Inf. Manag..

[15]  Alan Marshall,et al.  Analytical metric weight generation for multi-domain trust in autonomous underwater MANETs , 2016, 2016 IEEE Third Underwater Communications and Networking Conference (UComms).

[16]  Antonio Iera,et al.  From "smart objects" to "social objects": The next evolutionary step of the internet of things , 2014, IEEE Communications Magazine.

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

[18]  Jia Guo,et al.  Trust-Based Service Management for Social Internet of Things Systems , 2016, IEEE Transactions on Dependable and Secure Computing.

[19]  N. Truong,et al.  A Reputation and Knowledge Based Trust Service Platform for Trustworthy Social Internet of Things , 2016 .

[20]  Hyun-Woo Lee,et al.  Toward a Trust Evaluation Mechanism in the Social Internet of Things , 2017, Sensors.

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

[22]  Ciprian Dobre,et al.  Securing Vehicular Networks Based on Data-Trust Computation , 2011, 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[23]  Yu Sun,et al.  Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in Mobile-Sense Service , 2015, 2015 IEEE International Conference on Multimedia Big Data.

[24]  Hyun-Woo Lee,et al.  A computational model to evaluate honesty in social internet of things , 2017, SAC.

[25]  Feng Wu,et al.  SocialTrust: Enabling long-term social cooperation in peer-to-peer services , 2013, Peer-to-Peer Networking and Applications.

[26]  Ling Liu,et al.  PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities , 2004, IEEE Transactions on Knowledge and Data Engineering.

[27]  Horace Ho-Shing Ip,et al.  Enhancing collaborative intrusion detection networks against insider attacks using supervised intrusion sensitivity-based trust management model , 2017, J. Netw. Comput. Appl..

[28]  Ananthram Swami,et al.  LogitTrust : A Logit Regression-based Trust Model for Mobile Ad Hoc Networks , 2014 .