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.
Jiang, H., Gao, Y., Sarwar, S.M., GarzaPerez, L., Robin, M. (2022). Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity. In: Chang, SY., Bathen, L., Di Troia, F., Austin, T.H., Nelson, A.J. (eds) Silicon Valley Cybersecurity Conference. SVCC 2021. Communications in Computer and Information Science, vol 1536. Springer, Cham. https://doi.org/10.1007/978-3-030-96057-5_3. https://rdcu.be/cKJoy
Silicon Valley Cybersecurity Conference. SVCC 2021
Original published version available at https://doi.org/10.1007/978-3-030-96057-5_3
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