Privacy Preserving Machine Learning (PPML) allows interested parties to perform machine learning (ML) tasks collaboratively, and at the same time, ensures the privacy of individual private data. Privacy requirements might be inherent to the field of application, as is in the case of health care and financial sectors, or it could be imposed legally through policies such as the European Union General Data Protection Regulation (GDPR). These privacy concerns pose a major hindrance to the wide-scale use of ML as a tool. Consequently, there has been an increased interest in PPML as a potential solution. Widespread adoption of ML tools is also inhibited by the high computational demands of the ML algorithms. PPML, furthermore, makes the already compute-intensive ML algorithms even more demanding. As many everyday users lack the needed computing resources, outsourcing the ML tasks to better equipped, and more powerful servers is a preferred alternative. Towards this, Secure Outsourced Computation (SOC) promises to provide a feasible solution. It allows end-users to securely outsource computation to a set of specialized cloud servers guaranteeing privacy of the end-user’s data while tolerating reasonable collusion amongst the servers. Realisation of PPML in an SOC setting can be done by relying on techniques from Secure Multiparty Computation (MPC), which allows n mutually distrusting parties to perform computations collaboratively on their private inputs, so that an adversary controlling at most t parties, cannot learn any information beyond what is already allowed by the output of the computation. MPC for a small number of parties in the honest majority, specifically the setting of three (3PC) [5, 11, 2, 1, 10, 7, 3] and four parties (4PC) [4, 6, 8] has gained much attention, as it allows highly efficient constructions that use only light-weight primitives. The setting of honest majority allows us to achieve strong security guarantee of Guaranteed Output Delivery (GOD) (all parties obtain the output irrespective of adversary’s behaviour). Robustness or GOD becomes extremely crucial for real-world deployment and usage of PPML techniques. A protocol with weaker security than GOD can lead to denial of service (because of the actions of the adversary), and can result in heavy economic losses for the service provider and decreased participation from the data providers. Hence, for the seamless adoption of PPML solutions in the real-world, robustness of the protocol is of utmost importance. In this work , we propose an efficient PPML framework, via secure MPC over rings, in the SOC setting with three (3PC) and four servers (4PC), at most one of which can be maliciously corrupt. Our framework crucially relies on a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. The proposed 3PC is as fast as the best known 3PC framework BLAZE  which only manages to provides a weaker security guarantee of fairness (adversary gets the output if and only if the honest parties get it). We then extend the 3PC framework to the 4PC setting, which outperforms the 3PC setting by several orders of magnitude. In the 4PC setting, our protocol performs as good as the best known fair 4PC framework Trident  and twice as fast as the best known robust 4PC framework FLASH . To summarize, we provide a PPML framework with the highest security guarantee, which when compared to the state-of-the-art protocols in the analogous setting, preserves the communication cost in 3PC and improves upon the cost in 4PC setting.
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