The Round Complexity of Secure Computation Against Covert Adversaries

We investigate the exact round complexity of secure multiparty computation (MPC) against *covert* adversaries who may attempt to cheat, but do not wish to be caught doing so. Covert adversaries lie in between semi-honest adversaries who follow protocol specification and malicious adversaries who may deviate arbitrarily. Recently, two round protocols for semi-honest MPC and four round protocols for malicious-secure MPC were constructed, both of which are optimal. While these results can be viewed as constituting two end points of a security spectrum, we investigate the design of protocols that potentially span the spectrum. Our main result is an MPC protocol against covert adversaries with variable round complexity: when the detection probability is set to the lowest setting, our protocol requires two rounds and offers same security as semi-honest MPC. By increasing the detecting probability, we can increase the security guarantees, with round complexity five in the extreme case. The security of our protocol is based on standard cryptographic assumptions. We supplement our positive result with a negative result, ruling out *strict* three round protocols with respect to black-box simulation

Black-Box Transformations from Passive to Covert Security with Public Verifiability

In the context of secure computation, protocols with security against covert adversaries ensure that any misbehavior by malicious parties will be detected by the honest parties with some constant probability. As such, these protocols provide better security guarantees than passively secure protocols and, moreover, are easier to construct than protocols with full security against active adversaries. Protocols that, upon detecting a cheating attempt, allow the honest parties to compute a certificate that enables third parties to verify whether an accused party misbehaved or not are called publicly verifiable. In this work, we present the first generic compilers for constructing two-party protocols with covert security and public verifiability from protocols with passive security. We present two separate compilers, which are both fully blackbox in the underlying protocols they use. Both of them only incur a constant multiplicative factor in terms of bandwidth overhead and a constant additive factor in terms of round complexity on top of the passively secure protocols they use. The first compiler applies to all two-party protocols that have no private inputs. This class of protocols covers the important class of preprocessing protocols that are used to setup correlated randomness among parties. We use our compiler to obtain the first secret-sharing based two-party protocol with covert security and public verifiability. Notably, the produced protocol achieves public verifiability essentially for free when compared with the best known previous solutions based on secret-sharing that did not provide public verifiability Our second compiler constructs protocols with covert security and public verifiability for arbitrary functionalities from passively secure protocols. It uses our first compiler to perform a setup phase, which is independent of the parties\u27 inputs as well as the protocol they would like to execute. Finally, we show how to extend our techniques to obtain multiparty computation protocols with covert security and public verifiability against arbitrary constant fractions of corruptions

XONN: XNOR-based Oblivious Deep Neural Network Inference

Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge in this setting is to ensure the privacy of the clients' sensitive data. Oblivious inference is the task of running the neural network on the client's input without disclosing the input or the result to the server. This paper introduces XONN, a novel end-to-end framework based on Yao's Garbled Circuits (GC) protocol, that provides a paradigm shift in the conceptual and practical realization of oblivious inference. In XONN, the costly matrix-multiplication operations of the deep learning model are replaced with XNOR operations that are essentially free in GC. We further provide a novel algorithm that customizes the neural network such that the runtime of the GC protocol is minimized without sacrificing the inference accuracy. We design a user-friendly high-level API for XONN, allowing expression of the deep learning model architecture in an unprecedented level of abstraction. Extensive proof-of-concept evaluation on various neural network architectures demonstrates that XONN outperforms prior art such as Gazelle (USENIX Security'18) by up to 7x, MiniONN (ACM CCS'17) by 93x, and SecureML (IEEE S&P'17) by 37x. State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number of layers in the model. XONN is first to perform oblivious inference on Fitnet architectures with up to 21 layers, suggesting a new level of scalability compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to perform privacy-preserving medical diagnosis.Comment: To appear in USENIX Security 201

Finding Safety in Numbers with Secure Allegation Escrows

For fear of retribution, the victim of a crime may be willing to report it only if other victims of the same perpetrator also step forward. Common examples include 1) identifying oneself as the victim of sexual harassment, especially by a person in a position of authority or 2) accusing an influential politician, an authoritarian government, or ones own employer of corruption. To handle such situations, legal literature has proposed the concept of an allegation escrow: a neutral third-party that collects allegations anonymously, matches them against each other, and de-anonymizes allegers only after de-anonymity thresholds (in terms of number of co-allegers), pre-specified by the allegers, are reached. An allegation escrow can be realized as a single trusted third party; however, this party must be trusted to keep the identity of the alleger and content of the allegation private. To address this problem, this paper introduces Secure Allegation Escrows (SAE, pronounced "say"). A SAE is a group of parties with independent interests and motives, acting jointly as an escrow for collecting allegations from individuals, matching the allegations, and de-anonymizing the allegations when designated thresholds are reached. By design, SAEs provide a very strong property: No less than a majority of parties constituting a SAE can de-anonymize or disclose the content of an allegation without a sufficient number of matching allegations (even in collusion with any number of other allegers). Once a sufficient number of matching allegations exist, the join escrow discloses the allegation with the allegers' identities. We describe how SAEs can be constructed using a novel authentication protocol and a novel allegation matching and bucketing algorithm, provide formal proofs of the security of our constructions, and evaluate a prototype implementation, demonstrating feasibility in practice.Comment: To appear in NDSS 2020. New version includes improvements to writing and proof. The protocol is unchange

FFT-Based Deep Learning Deployment in Embedded Systems

Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the embedded platforms with intensive computation and storage. Researchers have investigated on reducing DNN model size with negligible accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN training and inference model suitable for embedded platforms with reduced asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. We develop the training and inference algorithms based on FFT as the computing kernel and deploy the FFT-based inference model on embedded platforms achieving extraordinary processing speed.Comment: Design, Automation, and Test in Europe (DATE) For source code, please contact Mahdi Nazemi at <[email protected]

Anomaly Detection in Network Streams Through a Distributional Lens

Anomaly detection in computer networks yields valuable information on events relating to the components of a network, their states, the users in a network and their activities. This thesis provides a unified distribution-based methodology for online detection of anomalies in network traffic streams. The methodology is distribution-based in that it regards the traffic stream as a time series of distributions (histograms), and monitors metrics of distributions in the time series. The effectiveness of the methodology is demonstrated in three application scenarios. First, in 802.11 wireless traffic, we show the ability to detect certain classes of attacks using the methodology. Second, in information network update streams (specifically in Wikipedia) we show the ability to detect the activity of bots, flash events, and outages, as they occur. Third, in Voice over IP traffic streams, we show the ability to detect covert channels that exfiltrate confidential information out of the network. Our experiments show the high detection rate of the methodology when compared to other existing methods, while maintaining a low rate of false positives. Furthermore, we provide algorithmic results that enable efficient and scalable implementation of the above methodology, to accomodate the massive data rates observed in modern infomation streams on the Internet. Through these applications, we present an extensive study of several aspects of the methodology. We analyze the behavior of metrics we consider, providing justification of our choice of those metrics, and how they can be used to diagnose anomalies. We provide insight into the choice of parameters, like window length and threshold, used in anomaly detection

Experimental preparation and verification of quantum money

A quantum money scheme enables a trusted bank to provide untrusted users with verifiable quantum banknotes that cannot be forged. In this work, we report an experimental demonstration of the preparation and verification of unforgeable quantum banknotes. We employ a security analysis that takes experimental imperfections fully into account. We measure a total of $3.6\times 10^6$ states in one verification round, limiting the forging probability to $10^{-7}$ based on the security analysis. Our results demonstrate the feasibility of preparing and verifying quantum banknotes using currently available experimental techniques.Comment: 12 pages, 4 figure