Gianclaudio explains why PETs alone are insufficient solutions for data protection and discusses the obstacles to achieving fairness in data processing – including bias, discrimination, social injustice, and market power imbalances. We discuss data alteration techniques such as anonymization, pseudonymization, synthetic data, and differential privacy in relation to GDPR compliance. Plus, Gianclaudio highlights the issues of representation for minorities in differential privacy and stresses the importance of involving these groups in identifying bias and assessing AI technologies. We also touch on the need for ongoing research on PETs to address these challenges and share our perspectives on the future of this research.
Topics Covered:
What inspired Gianclaudio to research fairness and PETs
How PETs are about power and control
The legal / GDPR and computer science perspectives on 'fairness'
How fairness relates to discrimination, social injustices, and market power imbalances
How data obfuscation techniques relate to AI / ML
How well the use of anonymization, pseudonymization, and synthetic data techniques address data protection challenges under the GDPR
How the use of differential privacy techniques may led to unfairness
Whether the use of encrypted data processing tools and federated and distributed analytics achieve fairness
3 main PET shortcomings and how to overcome them: 1) bias discovery; 2) harms to people belonging to protected groups and individuals autonomy; and 3) market imbalances.
Areas that warrant more research and investigation
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