Personal big data pricing method based on differential privacy

• Published in: Computers & security Vol. 113; p. 102529
• Main Authors: Shen, Yuncheng, Guo, Bing, Shen, Yan, Duan, Xuliang, Dong, Xiangqian, Zhang, Hong, Zhang, Chuanwu, Jiang, Yuming
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.02.2022; Elsevier Sequoia S.A
• Subjects: Big Data; Data privacy; Differential privacy; Personal big data; Positive pricing; Pricing; Privacy; Privacy budget; Privacy compensation; Privacy protection; Reverse pricing; Security; Data privacy; Differential privacy; Positive pricing; Privacy budget; Privacy protection; Privacy compensation; Personal big data; Reverse pricing
• ISSN: 0167-4048; 1872-6208
• DOI: 10.1016/j.cose.2021.102529

Personal big data can greatly promote social management, business applications, and personal services, and bring certain economic benefits to users. The difficulty with personal big data security and privacy protection lies in realizing the maximization of the value of personal big data and in striking a balance between data privacy protection and sharing on the premise of satisfying personal big data security and privacy protection. Thus, in this paper, we propose a personal big data pricing method based on differential privacy (PMDP). We design two different mechanisms of positive and reverse pricing to reasonbly price personal big data. We perform aggregate statistics on an open dataset and extensively evaluated its performance. The experimental results show that PMDP can provide reasonable pricing for personal big data and fair compensation to data owners, ensuring an arbitrage-free condition and finding a balance between privacy protection and data utility.