Privacy-Preserving Data Sharing Technologies
- 8 August 2025
- book chapter
- Published by IGI Global Scientific Publishing
- p. 449-472
Abstract
The quick advancement of big data sharing increases the importance of protecting data privacy. Differential privacy appeared in 2006 to become the fundamental privacy-preserving data mining and publication method. Various technologies, including federated learning, homomorphic encryption, and secure multi-party computation, have introduced new solutions for safeguarding data privacy during recent technological developments. Differential privacy is experiencing a comparison test in current technology markets because Google and Apple have integrated it into new products. This chapter begin by examining the differential privacy systems used in traditional centralized data models and their usage in data mining and publishing processes. The chapter investigates modern developments in local differential privacy by examining randomized response Bloom filters and statistical inference tools related to crowdsourcing systems.Keywords
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