IoT Expunge: Implementing Verifiable Retention of IoT Data

Autor:	Panwar, Nisha, Sharma, Shantanu, Gupta, Peeyush, Ghosh, Dhrubajyoti, Mehrotra, Sharad, Venkatasubramanian, Nalini
Rok vydání:	2020
Předmět:	Computer Science - Cryptography and Security Computer Science - Databases Computer Science - Distributed Parallel and Cluster Computing Computer Science - Data Structures and Algorithms
Druh dokumentu:	Working Paper
DOI:	10.1145/3374664.3375737
Popis:	The growing deployment of Internet of Things (IoT) systems aims to ease the daily life of end-users by providing several value-added services. However, IoT systems may capture and store sensitive, personal data about individuals in the cloud, thereby jeopardizing user-privacy. Emerging legislation, such as California's CalOPPA and GDPR in Europe, support strong privacy laws to protect an individual's data in the cloud. One such law relates to strict enforcement of data retention policies. This paper proposes a framework, entitled IoT Expunge that allows sensor data providers to store the data in cloud platforms that will ensure enforcement of retention policies. Additionally, the cloud provider produces verifiable proofs of its adherence to the retention policies. Experimental results on a real-world smart building testbed show that IoT Expunge imposes minimal overheads to the user to verify the data against data retention policies. Comment: This paper has been accepted in 10th ACM Conference on Data and Application Security and Privacy (CODASPY), 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.04969 Zobrazit plný text záznamu View this record from Arxiv