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1. Statistical Data Privacy: A Song of Privacy and Utility

   https://doi.org/10.1146/annurev-statistics-033121-112921  

   Slavković, Aleksandra ; Seeman, Jeremy ( March 2023 , Annual Review of Statistics and Its Application)

   To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data release mechanisms that sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC) and the more recent differential privacy (DP). Despite framing differences, both SDC and DP share the same statistical problems at their core. For inference problems, either we may design optimal release mechanisms and associated estimators that satisfy bounds on disclosure risk measures, or we may adjust existing sanitized output to create new statistically valid and optimal estimators. Regardless of design or adjustment, in evaluating risk and utility, valid statistical inferences from mechanism outputs require uncertainty quantification that accounts for the effect of the sanitization mechanism that introduces bias and/or variance. In this review, we discuss the statistical foundations common to both SDC and DP, highlight major developments in SDP, and present exciting open research problems in private inference.

    

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2. Privacy for All: Ensuring Fair and Equitable Privacy Protections

   Ekstrand, Michael D. ; Joshaghani, Rezvan ; Mehrpouyan, Hoda ( January 2018 , 1st Conference on Fairness, Accountability and Transparency)

   null (Ed.)

   In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and non-discriminatory to the privacy protections those systems may provide. Privacy literature seldom considers whether a proposed privacy scheme protects all persons uniformly, irrespective of membership in protected classes or particular risk in the face of privacy failure. Just as algorithmic decision-making systems may have discriminatory outcomes even without explicit or deliberate discrimination, so also privacy regimes may disproportionately fail to protect vulnerable members of their target population, resulting in disparate impact with respect to the effectiveness of privacy protections.We propose a research agenda that will illuminate this issue, along with related issues in the intersection of fairness and privacy, and present case studies that show how the outcomes of this research may change existing privacy and fairness research. We believe it is important to ensure that technologies and policies intended to protect the users and subjects of information systems provide such protection in an equitable fashion. 

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3. Disaster privacy/privacy disaster

   https://doi.org/10.1002/asi.24353  

   Sanfilippo, Madelyn R. ; Shvartzshnaider, Yan ; Reyes, Irwin ; Nissenbaum, Helen ; Egelman, Serge ( March 2020 , Journal of the Association for Information Science and Technology)
4. Robust Privacy-Utility Tradeoffs Under Differential Privacy and Hamming Distortion

   https://doi.org/10.1109/TIFS.2018.2831619  

   Kalantari, Kousha ; Sankar, Lalitha ; Sarwate, Anand D. ( November 2018 , IEEE Transactions on Information Forensics and Security)
5. The limits of pan privacy and shuffle privacy for learning and estimation

   https://doi.org/10.1145/3406325.3450995  

   Cheu, Albert ; Ullman, Jonathan ( January 2021 , Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing)

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