qu-apdpattackproof-2019.pdf (1.33 MB)
APDP: attack-proof personalized differential privacy model for a smart home
journal contribution
posted on 2019-01-01, 00:00 authored by Y Zhang, Youyang Qu, Longxiang GaoLongxiang Gao, T H Luan, Xi Zheng, S Chen, Yong XiangYong XiangThe proliferation of smart devices in recent years has led to novel smart home applications that upgrade traditional home appliances to intelligent units and automatically adapt their services without human assistance. In a smart home system, a central gateway is required to coordinate the functions of various smart home devices and allow bidirectional communications. However, the gateway may cause leakage of sensitive information unless proper privacy protections are applied. In this work, we first introduce a smart home model based on fog computing and secured by differential privacy. Then, we apply a personalized differential privacy scheme to provide privacy protection. Furthermore, we consider a collusion attack and propose our differential privacy model called APDP based on a modified Laplace mechanism and a Markov process to strengthen privacy protection, thus resisting the attack. Lastly, we perform extensive experiments based on the real-world datasets to evaluate the proposed APDP model.
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Journal
IEEE accessVolume
7Pagination
166593 - 166605Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
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ISSN
2169-3536eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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