We introduce a new MPC protocol to securely compute any functionality over an arbitrary black-box finite ring (which may not be commutative), tolerating t < n/3 active corruptions whileguaranteeing output delivery (G.O.D.). Our protocol is based on replicated secret-sharing, whose share size is known to grow exponentially with the number of parties n. However, even though the internal storage and computation in our protocol remains exponential, the communication complexity of our protocol is constant, except for a light constant-round check that is performed at the end before revealing the output.

Abstract We investigate two questions in this paper: First, we ask to what extent “MPC friendly” models are already supported by major Machine Learning frameworks such as TensorFlow or PyTorch. Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e.g., TensorFlow to design, train and test models that later have to be evaluated securely. Second, we ask to what extent the functionality for evaluating Neural Networks already exists in general-purpose MPC frameworks. These frameworks have received more scrutiny, are better documented and supported on more platforms. Furthermore, they are typically flexible in terms of the threat model they support. In contrast, most secure evaluation protocols in the literature are targeted to a specific threat model and their implementations are only a “proof-of-concept”, making it very hard for their adoption in practice. We answer both of the above questions in a positive way:We observe that the quantization techniques supported by both TensorFlow, PyTorch and MXNet can provide models in a representation that can be evaluated securely; and moreover, that this evaluation can be performed by a general purpose MPC framework. We perform extensive benchmarks to understand the exact trade-offs between different corruption models, network sizes and efficiency. These experiments provide an interesting insight into cost between active and passive security, as well as honest and dishonest majority. Our work shows then that the separating line between existing ML frameworks and existing MPC protocols may be narrower than implicitly suggested by previous works.

This paper presents an independent security review of a popular encrypted cloud storage service (ECS) SpiderOakONE. Contrary to previous work analyzing similar programs, we formally define a minimal security requirements for confidentiality in ECS which takes into account the possibility that the ECS actively turns against its users in an attempt to break the confidentiality of the users» data. Our analysis uncovered several serious issues, which either directly or indirectly damage the confidentiality of a user»s files, therefore breaking the claimed Zero- or No-Knowledge property (i.e., the claim that even the ECS itself cannot access the users» data). After responsibly disclosing the issues we found to SpiderOak, most have been fixed.