Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168434
Title: SPoFC: a framework for stream data aggregation with local differential privacy
Authors: Yang, Mengmeng
Lam, Kwok-Yan
Zhu, Tianqing
Tang, Chenghua
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Yang, M., Lam, K., Zhu, T. & Tang, C. (2023). SPoFC: a framework for stream data aggregation with local differential privacy. Concurrency and Computation: Practice and Experience, 35(5), e7572-. https://dx.doi.org/10.1002/cpe.7572
Journal: Concurrency and Computation: Practice and Experience 
Abstract: Collecting and analysing customers' data plays an essential role in the more intense market competition. It is critical to perform data analysis effectively while ensuring the user's privacy, especially after various privacy regulations are enacted. In this paper, we consider the problem of aggregating the stream data generated from wearable devices in a specific time period in a privacy-preserving manner. Specifically, we adopt the local differential privacy mechanism to provide a strong privacy guarantee for users. One major challenge is that all values of the stream need to be perturbed. The additive noise makes it hard to release an accurate data stream. One way to reduce the noise scale is to select some data points to perturb instead of all. The intuition is that more privacy budgets are applied to a single data point, which ensures the statistical accuracy. The perturbed data points are used to predict the un-selected data points without consuming an extra privacy budget. Based on this idea, we propose a novel stream data statistical framework, which includes four components, data fitting, skeleton point selection, noisy stream generation, and data aggregation. Extensive experiment results show that our proposed method achieves a much smaller mean square error given a fixed privacy budget compared with the state-of-the-art.
URI: https://hdl.handle.net/10356/168434
ISSN: 1532-0626
DOI: 10.1002/cpe.7572
Schools: School of Computer Science and Engineering 
Research Centres: Strategic Centre for Research in Privacy-Preserving Technologies & Systems
Rights: © 2022 John Wiley & Sons Ltd. All rights reserved. This is the peer reviewed version of the following article: Yang, M., Lam, K., Zhu, T. & Tang, C. (2023). SPoFC: a framework for stream data aggregation with local differential privacy. Concurrency and Computation: Practice and Experience, 35(5), e7572-, which has been published in final form at https://doi.org/10.1002/cpe.7572. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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