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Reliable customized privacy-preserving in Fog computing

conference contribution
posted on 2020-07-27, 00:00 authored by Wang (Tony) Xiaodong, B Gu, Youyang Qu, Y Ren, Yong XiangYong Xiang, Longxiang GaoLongxiang Gao
—Fog computing is an emergent computing paradigm that extends the cloud paradigm to the edge. With the explosive growth of smart devices and massive data generated everyday, cloud computing no longer matches the requirements of the Internet of Things (IoT) era, such as low latency, uninterrupted service and location awareness. Thus, fog computing has been introduced as a complement of the current cloud computing model to meet the requirements in IoT. Fog computing is
a relatively new networking paradigm and considered as a promising solution to support IoT scenarios. On the one hand, fog computing inherits many features from cloud; on the other hand, fog computing also inherits some challenges and issues from cloud computing: privacy issue is one of them. In this paper, we propose a personalized differential privacy model based on the distance between two fog nodes in a fog network. We also identify the collusion attack in differential privacy framework which compromised the personalized Laplace function. Based on that, we develop a personalized differential privacy model, which not only eliminate this particular attack but also optimize the trade-off between privacy preserving and data utility.

History

Event

IEEE ICC 2020 Communications. International Conference (2020 : Dublin, Ireland)

Volume

July 2020

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Location

Dublin, Ireland

Place of publication

Piscataway, N.J.

Start date

2020-06-07

End date

2020-06-11

ISSN

1550-3607

eISSN

1938-1883

ISBN-13

9781728150895

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2020, IEEE

Title of proceedings

ICC 2020 : Proceedings of the IEEE International Conference on Communications