Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity
This work addresses privacy preservation issues for researchers and practitioners in data-intensive fields, but it is incremental as it builds on existing differential privacy frameworks.
The paper identifies limitations and challenges of differential privacy in various applications, such as big data analysis and machine learning, and proposes combining it with dimension reduction and secure multiparty computing to define better privacy models.
Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models.