Synthetic Is All You Need: Removing the Auxiliary Data Assumption for Membership Inference Attacks Against Synthetic Data

Author:

Guépin FlorentORCID,Meeus MatthieuORCID,Creţu Ana-MariaORCID,de Montjoye Yves-AlexandreORCID

Publisher

Springer Nature Switzerland

Reference32 articles.

1. General data protection regulation (2016). https://gdpr-info.eu/

2. Annamalai, M.S.M.S., Gadotti, A., Rocher, L.: A linear reconstruction approach for attribute inference attacks against synthetic data. arXiv preprint arXiv:2301.10053 (2023)

3. Authority, F.C.: Synthetic data to support financial services innovation (2022). https://www.fca.org.uk/publication/call-for-input/synthetic-data-to-support-financial-services-innovation.pdf. Accessed 02 June 2023

4. Bellovin, S.M., Dutta, P.K., Reitinger, N.: Privacy and synthetic datasets. Stan. Tech. L. Rev. 22, 1 (2019)

5. Bukaty, P.: The California Consumer Privacy Act (CCPA): An Implementation Guide. IT Governance Publishing (2019). http://www.jstor.org/stable/j.ctvjghvnn

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