Supersonic OT: Fast Unconditionally Secure Oblivious Transfer
Oblivious Transfer (OT) is a fundamental cryptographic protocol with applications in secure Multi-Party Computation, Federated Learning, and Private Set Intersection. With the advent of quantum computing, it is crucial to develop unconditionally secure core primitives like OT to ensure their continued security in the post-quantum era. Despite over four decades since OT's introduction, the literature has predominantly relied on computational assumptions, except in cases using unconventional methods like noisy channels or a fully trusted party. Introducing "Supersonic OT", a highly efficient and unconditionally secure OT scheme that avoids public-key-based primitives, we offer an alternative to traditional approaches. Supersonic OT enables a receiver to obtain a response of size O(1). Its simple (yet non-trivial) design facilitates easy security analysis and implementation. The protocol employs a basic secret-sharing scheme, controlled swaps, the one-time pad, and a third-party helper who may be corrupted by a semi-honest adversary. Our implementation and runtime analysis indicate that a single instance of Supersonic OT completes in 0.35 milliseconds, making it up to 2000 times faster than the state-of-the-art base OT.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MOBIUS: Model-Oblivious Binarized Neural Networks
A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural network or encrypted pred…
BIG-bench Machine LearningmodelPredictionPrivacy PreservingPartially Oblivious Neural Network Inference
Oblivious inference is the task of outsourcing a ML model, like neural-networks, without disclosing critical and sensitive information, like the model's parameters. One of the most prominent solutions for secure obliviou…
Fast convolution algorithm for state space models
We present an unconditionally stable algorithm for applying matrix transfer function of a linear time invariant system (LTI) in time domain. The state matrix of an LTI system used for modeling long range dependencies in …
State Space ModelsSAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation
For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as \emph{secure aggregation}. However, secure aggregation makes mode…
Anomaly DetectionFederated LearningModel PoisoningCryptoTrain: Fast Secure Training on Encrypted Dataset
Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)-based non-inter-active training models …