Differential privacy

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Differential privacy is increasingly relevant to AI safety discussions around data governance, model training on sensitive data, and compliance with privacy regulations; this MSR page serves as an overview of the concept and related research.

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Importance: 62/100organizational reportreference

Summary

This Microsoft Research publication covers differential privacy, a mathematical framework that provides rigorous privacy guarantees when analyzing or publishing statistical information about datasets. It ensures that the inclusion or exclusion of any single individual's data has minimal impact on the output, protecting individual privacy while enabling aggregate analysis. The framework has become a foundational technique in privacy-preserving machine learning and data governance.

Key Points

•Differential privacy provides a formal mathematical definition of privacy, quantified by a parameter epsilon (ε) that bounds information leakage about individuals.
•Enables data analysis and machine learning on sensitive datasets while providing provable privacy guarantees rather than ad-hoc protections.
•Widely adopted in AI/ML pipelines (e.g., federated learning, model training) to prevent models from memorizing or leaking private training data.
•Relevant to AI governance and compliance frameworks requiring demonstrable privacy protections in automated decision systems.
•Represents a key technical tool for balancing data utility with individual privacy rights in large-scale AI deployments.

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# Differential Privacy

- Cynthia Dwork

**_33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006)_**

\| July 2006

Published by Springer Verlag

[Publication](http://dx.doi.org/10.1007/11787006_1)

[Download BibTex](https://www.microsoft.com/en-us/research/publication/differential-privacy/bibtex/)

In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an individual should be learnable from the database that cannot be learned without access to the database. We give a general impossibility result showing that a formalization of Dalenius’ goal along the lines of semantic security cannot be achieved. Contrary to intuition, a variant of the result threatens the privacy even of someone not in the database. This state of affairs suggests a new measure, differential privacy, which, intuitively, captures the increased risk to one’s privacy incurred by participating in a database. The techniques developed in a sequence of papers \[8, 13, 3\], culminating in those described in \[12\], can achieve any desired level of privacy under this measure. In many cases, extremely accurate information about the database can be provided while simultaneously ensuring very high levels of privacy.

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