“Anonymized data aren’t.” Either they are not really anonymized or the anonymization process destroys their utility. Aggregate statistics, too, can fail to protect privacy, sometimes spectacularly. Predictive models trained on large datasets memorize substantial portions of the training data and have been tricked into revealing this information. The US Census Bureau demonstrated a privacy attack against the statistics the Bureau itself published in 2010. Although there is provably(!) no magic bullet, Differential Privacy – a definition of privacy tailored to statistical data analysis and a collection of supporting algorithmic techniques -- has proven fruitful in a wide range of settings, from generating predictive text and emoji suggestions to publication of Census redistricting data.
Why is privacy so slippery? Why is this a new problem? What is Differential Privacy, and what happened when Alabama sued to prevent its use in the 2020 Decennial redistricting data?
Cynthia Dwork, Gordon McKay Professor of Computer Science at the John A. Paulson School of Engineering and Applied Sciences at Harvard, and Affiliated Faculty at the Harvard Law School and the Department of Statistics, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is Differential Privacy, a strong privacy guarantee permitting sophisticated data analysis. Differential Privacy is widely deployed in industry, including in every Apple device, and is the backbone of the Disclosure Avoidance System for the 2020 US Decennial Census.
Dwork joined Harvard after more than thirty years in Silicon Valley's industrial research labs. Some of her earliest work established the pillars on which every fault-tolerant system has been built for decades. Her innovations modernized cryptography to address the "wild West" of ungoverned interactions on the internet, provided a proof-of-concept for post-quantum cryptography, and formed the basis of crypto-currencies. Via a connection to differential privacy, she developed the first general approach to ensuring validity in exploratory data analysis, where the questions asked depend on the data themselves.
In 2012, Dwork initiated the theoretical investigation of algorithmic fairness, her current principal focus and the driving line of inquiry of her recently launched Hire Aspirations Institute, a multi-disciplinary endeavor devoted to fairness in hiring platforms.
Dwork is a member of the US National Academy of Sciences, the US National Academy of Engineering, and the American Philosophical Society, and a Fellow of the American Academy of Arts and Sciences and of the ACM. Her awards include the Gödel Prize, the ACM-IEEE Knuth Prize, the ACM Paris Kanellakis Theory and Practice Award, the RSA Mathematics Award, the IEEE Hamming Medal, and test-of-time recognition in four fields.