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Secure multi-party computation (MPC) techniques can be used to provide data privacy when users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art MPC techniques can be directly leveraged for DNN models that use simple activation functions (AFs) such as ReLU. However, these techniques are ineffective and/or inefficient for the complex and highly non-linear AFs used in cutting-edge DNN models. We present Compact, which produces piece-wise polynomial approximations of complex AFs to enable their efficient use with state-of-the-art MPC techniques. Compact neither requires nor imposes any restriction on model training and results in near-identical model accuracy. To achieve this, we design Compact with input density awareness, and use an application specific simulated annealing type optimization to generate computationally more efficient approximations of complex AFs. We extensively evaluate Compact on four different machine-learning tasks with DNN architectures that use popular complex AFs silu, gelu, and mish. Our experimental results show that Compact incurs negligible accuracy loss while being 2x-5x computationally more efficient than state-of-the-art approaches for DNN models with large number of hidden layers. Our work accelerates easy adoption of MPC techniques to provide user data privacy even when the queried DNN models consist of a number of hidden layers, and trained over complex AFs. 

We present a secure two-factor authentication (TFA) scheme based on the user’s possession of a password and a crypto-capable device. Security is “end-to-end” in the sense that the attacker can attack all parts of the system, including all communication links and any subset of parties (servers, devices, client terminals), can learn users’ passwords, and perform active and passive attacks, online and offline. In all cases the scheme provides the highest attainable security bounds given the set of compromised components. Our solution builds a TFA scheme using any Device-enhanced Password-authenticated Key Exchange (PAKE), defined by Jarecki et al., and any Short Authenticated String (SAS) Message Authentication, defined by Vaudenay. We show an efficient instantiation of this modular construction, which utilizes any password-based client-server authentication method, with or without reliance on public-key infrastructure. The security of the proposed scheme is proven in a formal model that we formulate as an extension of the traditional PAKE model. We also report on a prototype implementation of our schemes, including TLS-based and PKI-free variants, as well as several instantiations of the SAS mechanism, all demonstrating the practicality of our approach. Finally, we present a usability study evaluating the viability of our protocol contrasted with the traditional PIN-based TFA approach in terms of efficiency, potential for errors, user experience, and security perception of the underlying manual process. 1