An Efficient Data Privacy Protection System Based on Differential Privacy
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5435-3_58
Reference22 articles.
1. Zhang XJ, Meng XF (2014) Differential privacy in data publication and analysis. Jisuanji Xuebao/Chin J Comput 37(4):927–949. https://doi.org/10.3724/SP.J.1016.2014.00927
2. Goswami P, Madan S (2017) Privacy preserving data publishing and data anonymization approaches: a review. In: Proceeding—IEEE International conference on computing, communication & automation ICCCA 2017, vol, pp 139–142. https://doi.org/10.1109/CCAA.2017.8229787
3. Majeed A, Lee S (2021) Anonymization techniques for privacy preserving data publishing: a comprehensive survey. IEEE Access 9:8512–8545. https://doi.org/10.1109/ACCESS.2020.3045700
4. Hall R, Wasserman L, Rinaldo A (2013) Random differential privacy. J Priv Confidentiality 4(2):1–12. https://doi.org/10.29012/jpc.v4i2.621
5. Goryczka S, Xiong L (2017) A comprehensive comparison of multiparty secure additions with differential privacy. IEEE Trans Dependable Secur Comput 14(5):463–477. https://doi.org/10.1109/TDSC.2015.2484326
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