Machine learning classification is used in numerous settings nowadays, such as medical or genomics predictions, spam detection, face recognition, and financial predictions. Due to privacy concerns, in some of these applications, it is important that the data and the classifier remain confidential.In this work, we construct three major classification protocols that satisfy this privacy constraint: hyperplane decision, Naïve Bayes, and decision trees. We also enable these protocols to be combined with AdaBoost. At the basis of these constructions is a new library of building blocks for constructing classifiers securely; we demonstrate that this library can be used to construct other classifiers as well, such as a multiplexer and a face detection classifier.We implemented and evaluated our library and classifiers. Our protocols are efficient, taking milliseconds to a few seconds to perform a classification when running on real medical datasets.
Order-preserving encryption-an encryption scheme where the sort order of ciphertexts matches the sort order of the corresponding plaintexts-allows databases and other applications to process queries involving order over encrypted data efficiently. The ideal security guarantee for order-preserving encryption put forth in the literature is for the ciphertexts to reveal no information about the plaintexts besides order. Even though more than a dozen schemes were proposed, all these schemes leak more information than order. This paper presents the first order-preserving scheme that achieves ideal security. Our main technique is mutable ciphertexts, meaning that over time, the ciphertexts for a small number of plaintext values change, and we prove that mutable ciphertexts are needed for ideal security. Our resulting protocol is interactive, with a small number of interactions. We implemented our scheme and evaluated it on microbenchmarks and in the context of an encrypted MySQL database application. We show that in addition to providing ideal security, our scheme achieves 1-2 orders of magnitude higher performance than the state-of-the-art order-preserving encryption scheme, which is less secure than our scheme.
Garbled circuits, introduced by Yao in the mid 80s, allow computing a function f on an input x without leaking anything about f or x besides f (x). Garbled circuits found numerous applications, but every known construction suffers from one limitation: it offers no security if used on multiple inputs x. In this paper, we construct for the first time reusable garbled circuits. The key building block is a new succinct single-key functional encryption scheme.Functional encryption is an ambitious primitive: given an encryption Enc(x) of a value x, and a secret key sk f for a function f , anyone can compute f (x) without learning any other information about x. We construct, for the first time, a succinct functional encryption scheme for any polynomial-time function f where succinctness means that the ciphertext size does not grow with the size of the circuit for f , but only with its depth. The security of our construction is based on the intractability of the Learning with Errors (LWE) problem and holds as long as an adversary has access to a single key sk f (or even an a priori bounded number of keys for different functions).Building on our succinct single-key functional encryption scheme, we show several new applications in addition to reusable garbled circuits, such as a paradigm for general function obfuscation which we call token-based obfuscation, homomorphic encryption for a class of Turing machines where the evaluation runs in input-specific time rather than worst-case time, and a scheme for delegating computation which is publicly verifiable and maintains the privacy of the computation.
Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e.g., better medical research, or fraud detection). However, they often cannot share their plaintext datasets due to privacy concerns and/or business competition. In this paper, we design and build Helen, a system that allows multiple parties to train a linear model without revealing their data, a setting we call coopetitive learning. Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m − 1 out of m parties. Our evaluation shows that Helen can achieve up to five orders of magnitude of performance improvement when compared to training using an existing state-of-the-art secure multi-party computation framework.
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