Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computing the Edit-Distance between Unrooted Ordered Trees
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
High-Assurance Integrity Techniques for Databases
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
An Approach to Evaluate Data Trustworthiness Based on Data Provenance
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Query Processing Techniques for Compliance with Data Confidence Policies
SDM '09 Proceedings of the 6th VLDB Workshop on Secure Data Management
Demonstrating a lightweight data provenance for sensor networks
Proceedings of the 2012 ACM conference on Computer and communications security
Securing data provenance in body area networks using lightweight wireless link fingerprints
Proceedings of the 3rd international workshop on Trustworthy embedded devices
Towards semantic comparison of multi-granularity process traces
Knowledge-Based Systems
Editorial: OPQL: Querying scientific workflow provenance at the graph level
Data & Knowledge Engineering
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As sensor networks are being increasingly deployed in decision-making infrastructures such as battlefield monitoring systems and SCADA (Supervisory Control and Data Acquisition) systems, making decision makers aware of the trustworthiness of the collected data is a crucial. To address this problem, we propose a systematic method for assessing the trustworthiness of data items. Our approach uses the data provenance as well as their values in computing trust scores, that is, quantitative measures of trustworthiness. To obtain trust scores, we propose a cyclic framework which well reflects the inter-dependency property: the trust score of the data affects the trust score of the network nodes that created and manipulated the data, and vice-versa. The trust scores of data items are computed from their value similarity and provenance similarity. The value similarity comes from the principle that "the more similar values for the same event, the higher the trust scores". The provenance similarity is based on the principle that "the more different data provenances with similar values, the higher the trust scores". Experimental results show that our approach provides a practical solution for trustworthiness assessment in sensor networks.