Abstract
This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure.
Highlights
We live in the era when people expect seamless connectivity for everyone and to everything everywhere
We argue that continuously decreasing control that users have on their data needs to be addressed across multiple layers and we propose the idea of Personal Cloud architecture that improves privacy of storage as well as sharing of user data
The Cognitive Privacy module (CogPriv) of user’s Personal Cloud can monitor the local network access points and individually or though collaboration make decisions on the network interface via which to send the data depending on the privacy level required by the application
Summary
We live in the era when people expect seamless connectivity for everyone and to everything everywhere For the majority, this means that potentially personal and sensitive data may get transferred by the networks which can compromise user privacy in different ways [1]. We propose Cognitive Privacy (CogPriv) which allows different application services (hosted in different virtual containers within Personal Cloud (PC)) to route traffic via most suitable networks in order to avoid network segments that may compromise user privacy and redirect user communication towards more secure networks. The Cognitive Privacy module (CogPriv) of user’s Personal Cloud can monitor the local network access points and individually or though collaboration make decisions on the network interface via which to send the data (and whether to send the data) depending on the privacy level required by the application.
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