A Comprehensive Approach for Data Quality and Provenance in Sensor Networks


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Sensor networks enable real-time gathering of large amounts of data that can be mined and analyzed for taking critical actions. As such, sensor networks are a key component of decision-making infrastructures. A critical issue in this context is the trustworthiness of the data being collected. Data integrity and quality decide the trustworthiness of data. Data integrity can be undermined not only because of errors by users, measurement devices and applications, but also because of malicious subjects who may inject inaccurate data with the goal of deceiving the data users. A fundamental tradeoff exists between data quality and the cost to gather and protect this data, e.g., in terms of sensor node energy. This project focuses on a multi-faceted solution to the problem of assessing integrity of data streams in sensor networks, taking into account cost and energy constraints. Key elements of the solution are: (a) a cyclic framework supporting the assessment of sensor data trustworthiness based on provenance, and sensor trustworthiness based on data that sensors provide; (b) strategies for continuously updating trust scores of sensor data and nodes; (c) a game-theoretic model to analyze and mitigate the risks due to active adversaries that try to undermine data integrity; (d) protocols for sensor network sleep/wake scheduling and routing that balance the data quality and energy efficiency tradeoff. The project also includes the development of tools for assessing data trustworthiness, and experimental evaluation of the system performance. The research has impact on healthcare, homeland security, and applications in several other domains.


Elisa Bertino (Purdue University)

Sonia Famy (Purdue University)

Murat Kantarcioglu

Saiful Islam

Jyothsna Rachapalli

Huseyin Ulusoy

Related Publications

Zhou Y and Kantarcioglu M. Adversarial Learning with Bayesian Hierarchical Mixtures of Experts. SIAM Data Mining 2014. Pdf


Kuzu M, Islam M S, Kantarcioglu M. “Efficient privacy-aware search over encrypted databases”. ACM CODASPY 2014. Pdf


Kuzu M, Islam M S, Kantarcioglu M. “Inference attack against encrypted range queries on outsourced databases”. ACM CODASPY 2014. Pdf


Rachapalli J, Khadilkar V, Kantarcioglu M, Thuraisingham B. “Towards fine grained RDF access control”. ACM SACMAT 2014. Pdf


Islam M S , Nix R, Kantarcioglu M. “A game theoretic approach for adversarial pipeline monitoring using Wireless Sensor Networks”. IEEE IRI Conference. Las Vegas, NV. Pdf

Shastry A, Kantarcioglu M, Zhou Y, Thuraisingham B. “Randomizing Smartphone Malware Profiles against Statistical Mining Techniques”. IFIP DBSEC. Paris, France. Pdf

Zhou Y, Kantarcioglu M, Thuraisingham B. “Self-Training with Selection-by-Rejection”. IEEE ICDM. Brussels, Belgium. Pdf

Cadenhead, T; Kantarcioglu, M; Thuraisingham, B. "Scalable and Efficient Reasoning for Enforcing Role-Based Access Control," in 24th Annual Conference on Data and Applications Security and Privacy., v.6166, 2010, p. 209-224. Pdf

Kantarcioglu, Murat Bensoussan, Alain Hoe, SingRu(Celine). "When Do Firms Invest in Privacy-Preserving Technologies?", 06/01/2010-05/31/2011, "Decision and Game Theory for Security", 2010, "Lecture Notes in Computer Science, 2010, Volume 6442/2010, 72-86". Pdf

Kantarcioglu, M.; Nix, R. "Incentive Compatible Distributed Data Mining", 06/01/2010-05/31/2011, "2010 IEEE Second International Conference on Social Computing (SocialCom)", 2010, "vol., no., pp.735-742, 20-22". Pdf

Mustafa Canim, Murat Kantarcioglu, Bijit Hore, and Sharad Mehrotra. "Building disclosure risk aware query optimizers for relational databases", 06/01/2010-05/31/2011, 2010, "Proc. VLDB Endow. 3, 1-2 (September 2010), 13-24.". Pdf

Hyo Sang Lim, Gabriel Ghinita, Elisa Bertino, Murat Kantarcioglu. "A Game Theoretic Approach for High Assurance of Data Trustworthiness in Sensor Networks", 06/01/2010-05/31/2011, "IEEE ICDE". Pdf