Understanding Information Disclosure from Secure Computation Output: A Study of Average Salary Computation
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Secure multi-party computation have seen substantial performance improvements in recent years and are being increasingly used in commercial products. While a great deal of work was dedicated to improving their efficiency under standard security models, the threat models do not take into account information leakage from the output of secure function evaluation. Quantification of information disclosure about private inputs from observing the function outcome is the subject of this work. Motivated by the City of Boston gender pay gap studies, we focus on the computation of average salaries and determine information disclosure about a target's private input to an adversary for a number of distributions including log-normal, which is typically used for modeling salaries. We consequently evaluate information disclosure after a repeated evaluation of the function on overlapping inputs and provide recommendations for using the sum and average functions in secure computation applications in practice.
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