A Blockchain-Based Approach for Saving and Tracking Differential-Privacy Cost

An increasing amount of users' sensitive information is now being collected for analytics purposes. Differential privacy has been widely studied in the literature to protect the privacy of users' information. The privacy parameter bounds the information about the data set leaked by the noi...

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Bibliographic Details
Published in:IEEE internet of things journal 2021-06, Vol.8 (11), p.8865-8882
Main Authors: Zhao, Yang, Zhao, Jun, Kang, Jiawen, Zhang, Zehang, Niyato, Dusit, Shi, Shuyu, Lam, Kwok-Yan
Format: Article
Language:English
Subjects:
Algorithms
Blockchain
Budgets
Cryptography
data analytics
Datasets
Differential privacy
differential privacy (DP)
Gaussian mechanism
Internet of Things
Noise measurement
Privacy
Queries
Servers
Smart contracts
Tracking
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Summary:An increasing amount of users' sensitive information is now being collected for analytics purposes. Differential privacy has been widely studied in the literature to protect the privacy of users' information. The privacy parameter bounds the information about the data set leaked by the noisy output. Oftentimes, a data set needs to be used for answering multiple queries, so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of privacy budget spending, which should not exceed the given limit of privacy budget. Moreover, if a query has been answered before and is asked again on the same data set, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design an algorithm to reuse previous noisy responses if the same query is asked repeatedly. In particular, considering that different requests of the same query may have different privacy requirements, our algorithm can set the optimal reuse fraction of the old noisy response and add new noise to minimize the accumulated privacy cost. Furthermore, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. As a result, the owner of the data set will have full knowledge about how the data set has been used and be confident that no new privacy cost will be incurred for answering queries once the specified privacy budget is exhausted.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3058209