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Knapsack graph-based privacy checking for smart environments
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AI
DOI：10.1016/j.cose.2021.1022400167-4048/.png)
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Abstract
En 中文
A smart environment consisting of sensors and actuators that network a physical space makes the lives of users extremely convenient. In such environments, service providers collect, store, and process a huge amount of personal information from the users, and provide appropriate services in return. Processing personal information helps service providers dispense personalized services to users. The flow of personal information requires legal documents such as privacy preferences and privacy policies from the users and service providers, respectively. A software agent (to be installed either at the client's or server's end) performs the privacy checking (i.e., matches the service provider's privacy policies with the user's privacy preferences), and produces a decision based on a take it or leave it approach. Often, this check is too restrictive such that the user is denied the services, even if the privacy policies partially satisfy the user preferences. In this work, the privacy checking problem is envisaged as a knapsack problem, and to address it, we derive (i) knapsack privacy checking (KPC), and (ii) knapsack graph-based privacy checking (KPC-G) techniques. The system developed using these techniques finds the services that best match the user's privacy preferences as well as the service provider's policies, with a twofold functionality: (a) it recommends the best matching services based on user-supplied tolerance values, or (b) it solves an optimization problem to suggest services after adjusting the user-supplied tolerance values. Our experimental results demonstrate the effectiveness of the proposed model. (c) 2021 Published by Elsevier Ltd.
Keywords:
Privacy policy checking
User privacy
Data security
Optimization
Knapsack problem
Similarity measurement
Journal
C
IF:
5.4
Papers:
4.6K
Citations:
1.4W
