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Secure multi-party computation has seen substantial performance improvements in recent years and is being increasingly used in commercial products. While a significant amount of work was dedicated to improving its efficiency under standard security models, the threat models do not account for information leakage from the output of secure function evaluation. Quantifying 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, in this work, we focus on the computation of the average of salaries and quantify information disclosure about private inputs of one or more participants (the target) to an adversary via information-theoretic techniques. We study a number of distributions including log-normal, which is typically used for modeling salaries. We consequently evaluate information disclosure after repeated evaluation of the average function on overlapping inputs, as was done in the Boston gender pay study that ran multiple times, and provide recommendations for using the sum and average functions in secure computation applications. Our goal is to develop mechanisms that lower information disclosure about participants’ inputs to a desired level and provide guidelines for setting up real-world secure evaluation of this function. 

This work studies compilation of honest-majority semi-honest secure multi-party protocols secure up to additive attacks to maliciously secure computation with abort. Prior work concentrated on arithmetic circuits composed of addition and multiplication gates, while many practical protocols rely on additional types of elementary operations or gates to achieve good performance. In this work we revisit the notion of security up to additive attacks in the presence of additional gates such as random element generation and opening. This requires re-evaluation of functions that can be securely evaluated, extending the notion of protocols secure up to additive attacks, and re-visiting the notion of delayed verification that points to weaknesses in its prior use and designing a mitigation strategy. We transform the computation using dual execution to achieve security in the malicious model with abort and experimentally evaluate the difference in performance of semi-honest and malicious protocols to demonstrate the low cost. 

Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our approach achieves both of them. Specifically, we show how to implement floating-point superaccumulators using secure multi-party computation techniques, so that a number of participants holding secret shares of floating-point numbers can accurately compute their sum while keeping the individual values private. 

Secure multi-party computation has seen significant performance advances and increasing use in recent years. Techniques based on secret sharing offer attractive performance and are a popular choice for privacy-preserving machine learning applications. Traditional techniques operate over a field, while designing equivalent techniques for a ring Z_2^k can boost performance. In this work, we develop a suite of multi-party protocols for a ring in the honest majority setting starting from elementary operations to more complex with the goal of supporting general-purpose computation. We demonstrate that our techniques are substantially faster than their field-based equivalents when instantiated with a different number of parties and perform on par with or better than state-of-the-art techniques with designs customized for a fixed number of parties. We evaluate our techniques on machine learning applications and show that they offer attractive performance.