An efficient online histogram publication method for data streams with local differential privacy

IF 2.7 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers of Information Technology & Electronic Engineering Pub Date : 2024-08-30 DOI:10.1631/fitee.2300368
Tao Tao, Funan Zhang, Xiujun Wang, Xiao Zheng, Xin Zhao
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Abstract

Many areas are now experiencing data streams that contain privacy-sensitive information. Although the sharing and release of these data are of great commercial value, if these data are released directly, the private user information in the data will be disclosed. Therefore, how to continuously generate publishable histograms (meeting privacy protection requirements) based on sliding data stream windows has become a critical issue, especially when sending data to an untrusted third party. Existing histogram publication methods are unsatisfactory in terms of time and storage costs, because they must cache all elements in the current sliding window (SW). Our work addresses this drawback by designing an efficient online histogram publication (EOHP) method for local differential privacy data streams. Specifically, in the EOHP method, the data collector first crafts a histogram of the current SW using an approximate counting method. Second, the data collector reduces the privacy budget by using the optimized budget absorption mechanism and adds appropriate noise to the approximate histogram, making it possible to publish the histogram while retaining satisfactory data utility. Extensive experimental results on two different real datasets show that the EOHP algorithm significantly reduces the time and storage costs and improves data utility compared to other existing algorithms.

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针对具有局部差分隐私的数据流的高效在线直方图发布方法
目前,许多领域都出现了包含隐私敏感信息的数据流。虽然这些数据的共享和发布具有很大的商业价值,但如果直接发布这些数据,数据中的用户隐私信息就会泄露。因此,如何在滑动数据流窗口的基础上连续生成可发布的直方图(满足隐私保护要求)已成为一个关键问题,尤其是在将数据发送给不受信任的第三方时。现有的直方图发布方法必须缓存当前滑动窗口(SW)中的所有元素,因此在时间和存储成本方面都不能令人满意。我们的工作通过为本地差分隐私数据流设计一种高效的在线直方图发布(EOHP)方法来解决这一缺点。具体来说,在 EOHP 方法中,数据收集器首先使用近似计数法制作当前 SW 的直方图。其次,数据收集器利用优化的预算吸收机制减少隐私预算,并在近似直方图中添加适当的噪声,从而在发布直方图的同时保留令人满意的数据效用。在两个不同真实数据集上的大量实验结果表明,与其他现有算法相比,EOHP 算法大大降低了时间和存储成本,提高了数据效用。
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来源期刊
Frontiers of Information Technology & Electronic Engineering
Frontiers of Information Technology & Electronic Engineering COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.00
自引率
10.00%
发文量
1372
期刊介绍: Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.
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