Presence Of Malicious Adversaries Research Articles

New approach for efficient malicious multiparty private set intersection

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. MPSI has found wide applications in fields such as advertising, vertical federated learning, privacy-preserving data mining, and private contact tracing. However, existing MPSI solutions face challenges in terms of security and efficiency. Some MPSI schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical MPSI solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted participants.This paper introduces a novel MPSI scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard Universal Composability framework, capable of thwarting malicious adversaries. To demonstrate the efficiency of TH-PSI, we conduct a comprehensive series of experiments. We implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as 222. We compare TH-PSI with state-of-the-art MPSI schemes, ENOC21 and NTY21, with a specific focus on their efficiency in the presence of malicious adversaries. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants.

Resilient cluster consensus for discrete-time high-order multi-agent systems against malicious adversaries

This paper studies the problem of resilient cluster consensus for discrete-time high-order multi-agent systems in the presence of malicious adversaries. Based on a decomposition form of the system matrices, a high-order resilient cluster consensus algorithm is proposed. Necessary and sufficient conditions for the normal agents to reach resilient cluster consensus despite the influence of the malicious adversaries are provided. In contrast to the existing results, the proposed algorithm is suitable for general high-order multi-agent systems and can reduce the network redundancy needed to tolerate the same number of malicious adversaries. Two examples are simulated to validate the effectiveness of the theoretical results.

Resilient distributed optimization under mobile malicious attacks

This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

Pika: Secure Computation using Function Secret Sharing over Rings

Machine learning algorithms crucially depend on non-linear mathematical functions such as division (for normalization), exponentiation (for softmax and sigmoid), tanh (as an activation function), logarithm (for crossentropy loss), and square root (for back-propagation of normalization layers). However, when machine learning is performed over secure computation, these protocols incur a large communication overhead and high round complexity. In this work, we propose new multi-party computation (MPC) protocols for such functions. Our protocols achieve constant round complexity (3 for semi-honest, 4 for malicious), an order of magnitude lower communication (54 − 121× lower than prior art), and high concrete efficiency (2−1163× faster runtime). We rely on recent advances in function secret sharing (FSS) to construct these protocols. Our contributions can be summarized as follows: (1) A constant round protocol to securely evaluate nonlinear functions such as division, exponentiation, logarithm, and tanh (in comparison to prior art which uses round complexity proportional to the rounds of iterative methods/required precision) with high accuracy. This construction largely follows prior work in look-up style secure computation. (2) Our main contribution is the extension of the above protocol to be secure in the presence of malicious adversaries in the honest majority setting. We provide a malicious sketching protocol for FSS schemes that works over rings and in order to prove its security, we extend (and prove) a corresponding form of SchwartzZippel lemma over rings. This is the first such extension of the lemma and it can be of independent interest in other domains of secure computation. (3) We implement our protocol and showcase order of magnitude improvements in runtime and communication. Given the low round complexity and substantially lower communication, our protocols achieve even better performance over network constrained environments such as WAN. Finally, we showcase how such functions can lead to scalability in machine learning. Note that techniques presented are applicable beyond the application of machine learning as the protocols effectively present an efficient 1-out-of-N oblivious transfer or an efficient private information retrieval protocol.

Resilient Real-Valued Consensus in Spite of Mobile Malicious Agents on Directed Graphs

This article addresses novel real-valued consensus problems in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked agents. By adopting several mobile adversary models from the computer science literature, we develop protocols which can mitigate the influence of such malicious agents. The algorithms follow the class of mean subsequence reduced (MSR) algorithms, under which agents ignore the suspicious values received from neighbors during their state updates. Different from the static adversary models, even after the adversaries move away, the infected agents may remain faulty in their values, whose effects must be taken into account. We develop conditions on the network structures for both the complete and non-complete directed graph cases, under which the proposed algorithms are guaranteed to attain resilient consensus. The tolerance bound for network conditions becomes more strict as the adversaries are allowed to have more power. Extensive simulations are carried out over random graphs to verify the effectiveness of our approach when the information of the adversarial agents in terms of their models and numbers is unknown to the agents.

Verifiable Data Mining Against Malicious Adversaries in Industrial Internet of Things

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.

Permutable Cut-and-Choose Oblivious Transfer and Its Application

Oblivious transfer (OT) is a significant primitive with wide use in secure two-party computation, private set intersection private and other cryptographic schemes. In the past ten years, different variants of OT primitive like cut-and-choose OT (CCOT) and outsourced OT (OOT), have been proposed so as to satisfy various emerging models. In this paper, we firstly propose and formalize a new primitive called permutable cut-and-choose OT (PCCOT) which generalizes the original CCOT functionality. Furthermore, we construct an efficient PCCOT protocol in the presence of malicious adversaries using the Decisional Diffie-Hellman (DDH) hard assumption. It is worth mentioning that we apply the PCCOT primitive to the efficient construction of secure wildcard pattern matching (WPM) protocol. The WPM functionality allows a party to determine the locations of its pattern with wildcard characters occurs in a long text of another party while revealing nothing to either party in addition to the length of their own inputs. Our proposed secure WPM protocol via PCCOT is secure against semi-honest adversary with 2 rounds and has identical communication cost as the the state-of-the-art scheme.

Efficient Constant-Round Multi-party Computation Combining BMR and SPDZ

Recently, there has been huge progress in the field of concretely efficient secure computation, even while providing security in the presence of malicious adversaries. This is especially the case in the two-party setting, where constant-round protocols exist that remain fast even over slow networks. However, in the multi-party setting, all concretely efficient fully-secure protocols, such as SPDZ, require many rounds of communication.

Zero knowledge proof for secure two-party computation with malicious adversaries in distributed networks

Distributed networks remarkably enhance the convenience of network connectivity. How to achieve efficient cooperative computation while preserving data privacy is a challenge in the scenario of distributed networks. Secure computation, as the key technology of information security and privacy protection in the distributed networks, attracts more and more attention. In the paper, we develop the protocols of secure two-party computation in the presence of malicious adversaries which are constructed with homomorphic probabilistic cryptosystem, and propose four honest-verifier zero-knowledge proof protocols to detect two cheating behaviours of the malicious adversary. The proposed protocols are more targeted than the existing work. The analysis shows that the proposed protocols are complete, sound and zero-knowledge. As an application, we show how to use our protocols in a secure two-party protocol to detect cheating, which can make it secure in the presence of malicious adversaries.

Zero knowledge proof for secure two-party computation with malicious adversaries in distributed networks

Distributed networks remarkably enhance the convenience of network connectivity. How to achieve efficient cooperative computation while preserving data privacy is a challenge in the scenario of distributed networks. Secure computation, as the key technology of information security and privacy protection in the distributed networks, attracts more and more attention. In the paper, we develop the protocols of secure two-party computation in the presence of malicious adversaries which are constructed with homomorphic probabilistic cryptosystem, and propose four honest-verifier zero-knowledge proof protocols to detect two cheating behaviours of the malicious adversary. The proposed protocols are more targeted than the existing work. The analysis shows that the proposed protocols are complete, sound and zero-knowledge. As an application, we show how to use our protocols in a secure two-party protocol to detect cheating, which can make it secure in the presence of malicious adversaries.

Oblivious Polynomial Evaluation and Secure Set-Intersection from Algebraic PRFs

In this paper, we study the two fundamental functionalities oblivious polynomial evaluation in the exponent and set-intersection and introduce a new technique for designing efficient secure protocols for these problems (and others). Our starting point is the technique (Benabbas et al. in CRYPTO, 2011) for verifiable delegation of polynomial evaluations, using algebraic PRFs. We use this tool, that is useful to achieve verifiability in the outsourced setting, in order to achieve privacy in the standard two-party setting. Our results imply new simple and efficient oblivious polynomial evaluation (OPE) protocols. We further show that our OPE protocols are readily used for secure set-intersection, implying much simpler protocols in the plain model. As a side result, we demonstrate the usefulness of algebraic PRFs for various search functionalities, such as keyword search and oblivious transfer with adaptive queries. Our protocols are secure under full simulation-based definitions in the presence of malicious adversaries.

Secure Localization in the Presence of Colluders in WSNs.

We address the challenge of correctly estimating the position of wireless sensor network (WSN) nodes in the presence of malicious adversaries. We consider adversarial situations during the execution of node localization under three classes of colluding adversaries. We describe a decentralized algorithm that aims at determining the position of nodes in the presence of such colluders. Colluders are assumed to either forge or manipulate the information they exchange with the other nodes of the WSN. This algorithm allows location-unknown nodes to successfully detect adversaries within their communication range. Numeric simulation is reported to validate the approach. Results show the validity of the proposal, both in terms of localization and adversary detection.

The STREAM Mechanism for CPS Security <italic>The Case of the Smart Grid</italic>

Cyber-physical systems (CPSs) integrate computation, communication, and physical capabilities to interact with the physical world and humans. In this paper, we develop a novel strategic resource availability management (STREAM) system to improve information integrity and availability in an energy constrained CPS environment under the presence of malicious adversaries. The term “resource” here can be any component of a CPS. The main elements of STREAM are: 1) difficult but realistic “repeated (adversary-defender) game” settings and 2) a set of provably optimal defender strategies plus effective heuristics, against equally potent adversary moves. STREAM is based on the concept of dynamic games in sequential game theory, and is the first system to incorporate the realistic behavioral aspect that in many CPSs, both, the class of adversaries, as well as the class of CPS protectors, could move in a covert and stealthy manner in order to outwit the other in the war on “resource control.” In order to demonstrate the effectiveness of STREAM strategies to improve CPS resource availability to the nonadversary, we first conduct a thorough theoretical analysis on a model smart grid CPS as a representative example of a CPS. We then follow up the analysis with an extensive simulation study on the standard IEEE 14 smart power grid architecture. The results show that STREAM strategies improve smart grid system integrity and availability by approximately upto 67% when compared to nonstrategic approaches. Our proposed (analysis, simulation) suite for the grid is extendible to general CPS application domains.

Private Over-Threshold Aggregation Protocols over Distributed Datasets

In this paper, we revisit the private over-threshold data aggregation problem. We formally define the problem's security requirements as both data and user privacy goals. To achieve both goals, and to strike a balance between efficiency and functionality, we devise an efficient cryptographic construction and its proxy-based variant. Both schemes are provably secure in the semi-honest model. Our key idea for the constructions and their malicious variants is to compose two encryption functions tightly coupled in a way that the two functions are commutative and one public-key encryption has an additive homomorphism. We call that double encryption. We analyze the computational and communication complexities of our construction, and show that it is much more efficient than the existing protocols in the literature. Specifically, our protocol has linear complexity in computation and communication with respect to the number of users. Its round complexity is also linear in the number of users. Finally, we show that our basic protocol is efficiently transformed into a stronger protocol secure in the presence of malicious adversaries, and provide the resulting protocol's performance and security analysis.

Efficient oblivious transfer with adaptive queries in UC framework

AbstractWe propose two efficient universally composable adaptivek‐out‐of‐N protocols, secure in the presence of malicious adversary in static corruption model under the Decision Linear and q‐Strong Diffie–Hellman assumptions. Our second scheme additionally requires the hardness of Decision Bilinear Diffie‐Hellman problem. Non‐interactive Groth–Sahai proofs are employed for the verification of pairing product equations. More interestingly, our first scheme facilitates the receiver to verify the correctness of messages recovered. The proposed protocols exhibit significant computational and communication efficiency compared with existing similar schemes in the literature. Copyright © 2016 John Wiley & Sons, Ltd.

Fast Cut-and-Choose-Based Protocols for Malicious and Covert Adversaries

In the setting of secure two-party computation, two parties wish to securely compute a joint function of their private inputs, while revealing only the output. One of the primary techniques for achieving efficient secure two-party computation is that of Yao's garbled circuits (FOCS 1986). In the semi-honest model, where just one garbled circuit is constructed and evaluated, Yao's protocol has proven itself to be very efficient. However, a malicious adversary who constructs the garbled circuit may construct a garbling of a different circuit computing a different function, and this cannot be detected (due to the garbling). In order to solve this problem, many circuits are sent and some of them are opened to check that they are correct while the others are evaluated. This methodology, called cut-and-choose, introduces significant overhead, both in computation and in communication, and is mainly due to the number of circuits that must be used in order to prevent cheating. In this paper, we present a cut-and-choose protocol for secure computation based on garbled circuits, with security in the presence of malicious adversaries, that vastly improves on all previous protocols of this type. Concretely, for a cheating probability of at most $$2^{-40}$$2-40, the best previous works send between 125 and 128 circuits. In contrast, in our protocol 40 circuits alone suffice (with some additional overhead). Asymptotically, we achieve a cheating probability of $$2^{-s}$$2-s where $$s$$s is the number of garbled circuits, in contrast to the previous best of $$2^{-0.32s}$$2-0.32s. We achieve this by introducing a new cut-and-choose methodology with the property that in order to cheat, all of the evaluated circuits must be incorrect, and not just the majority as in previous works. The security of our protocol relies on the decisional Diffie---Hellman assumption.

Secure linear system computation in the presence of malicious adversaries

In this paper, we study the system of linear equation problems in the two-party computation setting. Consider that P 1 holds an m × m matrix M 1 and an m-dimensional column vector B 1. Similarly, P 2 holds M 2 and B 2. Via executing a secure linear system computation, P 1 gets the output x (or ⊥) conditioned on (M 1 + M 2)x = (B 1 + B 2), and the rank of matrix M 1 + M 2, while P 2 gets nothing. This also can be used to settle other cooperative linear system problems. We firstly design an efficient protocol to solve this problem in the presence of malicious adversaries, then propose a simple way to modify our protocol for having a precise functionality, in which the rank of matrix M 1+M 2 is not necessary. We note that our protocol is more practical than these existing malicious secure protocols. We also give comparisons with other protocols and extensions to similar functions.

An Efficient Protocol for Secure Two-Party Computation in the Presence of Malicious Adversaries

We show an efficient secure two-party protocol, based on Yao’s construction, which provides security against malicious adversaries. Yao’s original protocol is only secure in the presence of semi-honest adversaries, and can be transformed into a protocol that achieves security against malicious adversaries by applying the compiler of Goldreich, Micali, and Wigderson (the “GMW compiler”). However, this approach does not seem to be very practical as it requires using generic zero-knowledge proofs. Our construction is based on applying cut-and-choose techniques to the original circuit and inputs. Security is proved according to the ideal/real simulation paradigm, and the proof is in the standard model (with no random oracle model or common reference string assumptions). The resulting protocol is computationally efficient: the only usage of asymmetric cryptography is for running \(O(1)\) oblivious transfers for each input bit (or for each bit of a statistical security parameter, whichever is larger). Our protocol combines techniques from folklore (like cut-and-choose) along with new techniques for efficiently proving consistency of inputs. We remark that a naive implementation of the cut-and-choose technique with Yao’s protocol does not yield a secure protocol. This is the first paper to show how to properly implement these techniques, and to provide a full proof of security. Our protocol can also be interpreted as a constant-round black-box reduction of secure two-party computation to oblivious transfer and perfectly hiding commitments, or a black-box reduction of secure two-party computation to oblivious transfer alone, with a number of rounds which is linear in a statistical security parameter. These two reductions are comparable to Kilian’s (20th STOC, 1988) reduction, which uses OT alone but incurs a number of rounds which is linear in the depth of the circuit.

Secure Hamming distance based record linkage with malicious adversaries

Record linkage aims at finding the matching records from two or multiple different databases. Many approximate string matching methods in privacy-preserving record linkage have been presented. In this paper, we study the problem of secure record linkage between two data files in the two-party computation setting. We note that two records are linked when all the Hamming distances between their attributes are smaller than some fixed thresholds. We firstly construct two efficient secure protocols for computing the powers of an encrypted value and implementing zero test on an encrypted value, then present an efficient protocol within constant rounds for computing the Hamming distance based record linkage in the presence of malicious adversaries by transferring these two protocols. We also discuss the extension of our protocol for settling the Levenshtein distance based record linkage problem.

Secure similarity coefficients computation for binary data and its extensions

SUMMARYSimilarity measures play an important role in classification problems, cluster analysis, and identification issues. This paper studies the secure similarity coefficients computation in the two‐party setting. Recently, a privacy‐preserving similarity coefficients protocol for binary data was proposed by Wong and Kim (Computers and Mathematics with Application 2012). We point out that their protocol is not secure, even in the semi‐honest model. In their protocol, the client can retrieve the inputs of the server without deviating from the protocol. Next, we propose a secure similarity coefficients computation protocol in the presence of malicious adversaries, which solves the same similarity coefficients functionality as that proposed by Wong and Kim. Meanwhile, we prove the protocol secure against the malicious adversaries by using the standard simulation‐based security definitions for secure two‐party computation. Also several extensions of our protocol for settling other specific problems are discussed. At last, we present a protocol computing the similarity coefficients with better privacy by using the secure integer division on ciphertexts. Copyright © 2013 John Wiley & Sons, Ltd.