We present techniques for constructing zero-knowledge argument systems from garbled circuits, extending a theoretical framework by Hazay and Venkitasubramaniam (J. Crypto, 2020) to the following directions:

• Our scheme is a hybrid, commit-and-prove ...

This paper investigates efficient and confidential circuit garbling techniques. The primary contribution of this research is the introduction of GPGRR2 (Gate Privacy preserving Garbled Row Reduction), a technique aimed at constructing garbled ...

The emerging field of cryptography, Secure Computation, can be used to solve a number of distributed computing applications without loss of privacy of sensitive/private data. The applications can be simple as coin tossing, agreement between parties, or ...

Data stored on centralized cloud servers may have some risks. Moreover, it may leak the data access pattern when accessing data on cloud servers. Oblivious RAM (ORAM) is a candidate solution to hide the data access pattern, but it inherently induces ...

This article presents the first privacy-preserving localization method based on provably secure primitives for smart automotive systems. Using this method, a car that is lost due to unavailability of GPS can compute its location with assistance from ...

This paper presents MAXelerator, the first hardware accelerator for privacy-preserving machine learning (ML) on cloud servers. Cloud-based ML is being increasingly employed in various data sensitive scenarios. While it enhances both efficiency and ...

This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL) framework that is built upon automated design, efficient logic synthesis, and optimization methodologies. DeepSecure targets scenarios in which neither of the ...

We propose a simple and efficient framework for obtaining efficient constant-round protocols for maliciously secure two-party computation. Our framework uses a function-independent preprocessing phase to generate authenticated information for the two ...

We propose a new, constant-round protocol for multi-party computation of boolean circuits that is secure against an arbitrary number of malicious corruptions. At a high level, we extend and generalize recent work of Wang et al. in the two-party setting. ...

This paper presents the first provably secure localization method for smart automotive systems. Using this method, a lost car can compute its location with assistance from three nearby cars while the locations of all the participating cars including the ...

We present GarbledCPU, the first framework that realizes a hardware-based general purpose sequential processor for secure computation. Our MIPS-based implementation enables development of applications (functions) in a high-level language while ...

The emergence of cloud computing provides an unlimited computation/storage for users, and yields new opportunities for multimedia analysis and retrieval research. However, privacy of users, e.g., search intention, may be leaked to the server and ...

This paper introduces the first efficient, scalable, and practical method for privacy-preserving k-nearest neighbors (k-NN) search. The approach enables performing the widely used k-NN search in sensitive scenarios where none of the parties reveal their ...

With the advent of cloud computing, secure outsourcing techniques of sequence comparisons are becoming increasingly valuable, especially for clients with limited resources. One of the most critical functionalities in data outsourcing is verifiability. ...

Based on the verifiable encryption and zero-knowledge proof protocols of Jarecki and Shmatikov and Pedersen’s verifiable threshold secret sharing scheme, this paper proposes a new full fair secure two-party computation protocols. For getting full fair, ...

Randomizing polynomials allow representing a function f ( x ) by a low-degree randomized mapping $$\hat{f}(x, r)$$ whose output distribution on an input x is a randomized encoding of f ( x ). It is known that any function f in uniform $$\bigoplus$$ L/poly (and in ...