PSI (Ψ): a Private data Sharing Interface

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Prochlo: Strong Privacy for Analytics in the Crowd (2017, methodology): “The analyzer, which has the most complete view of the reported data, may choose from a wide variety of differentially private mechanisms and data processing systems [27,31,38, 53, 63].”
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale (2020, background): “Another related privacy system is PSI (Ψ) from the Harvard Privacy Tools Project [15].”
CoinPress: Practical Private Mean and Covariance Estimation (2020, unknown): “Requiring users to provide such a priori bounds is a major challenge in systems for differentially private analysis [21].”
PROCHLO: Strong Privacy for Analytics in the Crowd (2017, methodology): “The analyzer, which has the most complete view of the reported data, may choose from a wide variety of differentially private mechanisms and data processing systems [27,31,38, 53, 63].”
Finite Sample Differentially Private Confidence Intervals (2017, methodology): “We came to the problem of constructing confidence intervals for a normal mean as part of an effort to bring differential privacy to practice in the sharing of social science research data through the design of the software tool PSI (Gaboardi et al., 2016a), as confidence intervals are a workhorse of”