Private Function Evaluation (PFE) enables two parties to jointly execute a computation such that one of them provides the input while the other chooses the function to compute. According to the traditional security requirements, a PFE protocol should leak no more information, neither about the function nor the input, than what is revealed by the output of the computation. Existing PFE protocols inherently restrict the scope of computable functions to a certain function class with given output size, thus ruling out the direct evaluation of such problematic functions as the identity map, which would entirely undermine the input privacy requirement. We observe that when not only the input x is confidential but certain partial information g(x) of it as well, standard PFE fails to provide meaningful input privacy if g and the function f to be computed fall into the same function class.
Private Function Evaluation (PFE) enables two parties to jointly execute a computation such that one of them provides the input while the other chooses the function to compute. According to the traditional security requirements, a PFE protocol should leak no more information, neither about the function nor the input, than what is revealed by the output of the computation. Existing PFE protocols inherently restrict the scope of computable functions to a certain function class with given output size, thus ruling out the direct evaluation of such problematic functions as the identity map, which would entirely undermine the input privacy requirement. We observe that when not only the input x is confidential but certain partial information g(x) of it as well, standard PFE fails to provide meaningful input privacy if g and the function f to be computed fall into the same function class.
We consider the problem of private function evaluation (PFE) in the two-party setting. Here, informally, one party holds an input x while the other holds a circuit describing a function f; the goal is for one (or both) of the parties to learn f(x) while revealing nothing more to either party. In contrast to the usual setting of secure computation — where the function being computed is known to both parties — PFE is useful in settings where the function (i.e., algorithm) itself must remain secret, e.g., because it is proprietary or classified. It is known that PFE can be reduced to standard secure computation by having the parties evaluate a universal circuit, and this is the approach taken in most prior work. Using a universal circuit, however, introduces additional overhead and results in a more complex implementation. We show here a completely new technique for PFE that avoids universal circuits, and results in constant-round protocols with communication/computational complexity linear in the size of the circuit computing f. This gives the first constant-round protocol for PFE with linear complexity (without using fully homomorphic encryption), even restricted to semi-honest adversaries. 1
Outsourced secure multiparty computation (SMC) protocols allow resource-constrained devices to execute input-private computation with great efficiency. Unfortunately, existing outsourced SMC protocols require that all parties know the function being evaluated, precluding applications where the function itself must remain private. We develop the first linear-complexity protocols for outsourcing private function evaluation (PFE), SMC protocols that provide input and function privacy. Assuming a semi-honest function holder, we build on existing two-party PFE constructions to develop outsourced protocols that are secure against a semi-honest, covert, or malicious outsourcing server and malicious mobile participants. To do this, we develop a garbling technique for combining public and private sub-circuits in a single computation. This allows us to apply auxiliary checks for malicious behaviour using only free-XOR gates. These protocols demonstrate the feasibility of outsourced PFE and provide a first step towards privacy-preserving applications for use in cloud computing.
Private function evaluation aims to securely compute a functionf(x1,…,xn)without leaking any information other than what is revealed by the output, wherefis a private input of one of the parties (sayParty1) andxiis a private input of theith partyPartyi. In this article, we propose a novel and securetwo-party private function evaluation(2PFE) scheme based on the DDH assumption. Our scheme introduces a reusability feature that significantly improves the state-of-the-art. Accordingly, our scheme has two variants, one is utilized in the initial execution of the functionf, and the other is utilized in its subsequent evaluations. To the best of our knowledge, this is the first and most efficient 2PFE scheme that enjoys a reusablity feature. Our protocols achieve linear communication and computation complexities and a constant number of rounds which is at most three.
Private function evaluation (PFE) is a special case of secure multi-party computation (MPC), where the function to be computed is known by only one party. PFE is useful in several real-life applications where an algorithm or a function itself needs to remain secret for reasons such as protecting intellectual property or security classification level. In this paper, we focus on improving 2-party PFE based on symmetric cryptographic primitives. In this respect, we look back at the seminal PFE framework presented by Mohassel and Sadeghian at Eurocrypt’13. We show how to adapt and utilize the well-known half gates garbling technique (Zahur et al., Eurocrypt’15) to their constant-round 2-party PFE scheme. Compared to their scheme, our resulting optimization significantly improves the efficiency of both the underlying Oblivious Evaluation of Extended Permutation (OEP) and secure 2-party computation (2PC) protocols, and yields a more than 40% reduction in overall communication cost (the computation time is also slightly decreased and the number of rounds remains unchanged).
Development of computing devices with the proliferation of the Internet has prompted enormous opportunities for cooperative computation. These computations could occur between trusted or partially trusted partners, or even between competitors. Secure multi-party computation (MPC) protocols allow two or more parties to collaborate and compute a public functionality using their private inputs without the need for a trusted third-party. However, the generic solutions for MPC are not adequate for some particular cases where the function itself is also sensitive and required to be kept private. Private function evaluation (PFE) is a special case of MPC, where the function to be computed is known by only one party. PFE is useful in several real-life applications where an algorithm or a function itself needs to remain secret for reasons such as protecting intellectual property or security classification level. Recently, designing efficient PFE protocols have been a challenging and attractive task for cryptography researchers. iv In this dissertation, we mainly focus on improving two-party private function evaluation (2PFE) schemes. Our primary goal is enhancing the state-of-the-art by designing secure and cost-efficient 2PFE protocols for both symmetric and asymmetric cryptography based solutions. In this respect, we first aim to improve 2PFE protocols based on (mostly) symmetric cryptographic primitives. We look back at the seminal PFE framework presented by Mohassel and Sadeghian at Eurocrypt'13. We show how to adapt and utilize the well-known half gates garbling technique (Zahur et al., Eurocrypt'15) to their constant round 2PFE scheme. Compared to their scheme, our resulting optimization significantly improves both underlying oblivious extended permutation (OEP) and secure 2-party computation (2PC) protocols, and yields a more than 40% reduction in overall communication cost. We next propose a novel and highly efficient 2PFE scheme based on the decisional Di e-Hellman (DDH) assumption. Our scheme consists of two protocols, one is utilized in the initial execution, and the other is in the subsequent runs. One of the novelties of our scheme over the state-of-the-art is that it results in a significant cost reduction when the same private function is evaluated more than once between the same or varying parties. To the best of our knowledge, this is the most efficient and the first 2PFE scheme that enjoys reusability feature. Our protocols achieve linear communication and computation complexities, and a constant number of rounds which is at most three (depending on the size of the inputs of the party that holds the function).