Honest Participants Research Articles

SMPTC3: Secure Multi-Party Protocol Based Trusted Cross-Chain Contracts

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

The effects of persuasion principles on perceived honesty during shoulder surfing attacks

Purpose This study aimed to investigate how honest participants perceived an attacker to be during shoulder surfing scenarios that varied in terms of which Principle of Persuasion in Social Engineering (PPSE) was used, whether perceived honesty changed as scenarios progressed, and whether any changes were greater in some scenarios than others. Design/methodology/approach Participants read one of six shoulder surfing scenarios. Five depicted an attacker using one of the PPSEs. The other depicted an attacker using as few PPSEs as possible, which served as a control condition. Participants then rated perceived attacker honesty. Findings The results revealed honesty ratings in each condition were equal during the beginning of the conversation, participants in each condition perceived the attacker to be honest during the beginning of the conversation, perceived attacker honesty declined when the attacker requested the target perform an action that would afford shoulder surfing, perceived attacker honesty declined more when the Distraction and Social Proof PPSEs were used, participants perceived the attacker to be dishonest when making such requests using the Distraction and Social Proof PPSEs and perceived attacker honesty did not change when the attacker used the target’s computer. Originality/value To the best of the authors’ knowledge, this experiment is the first to investigate how persuasion tactics affect perceptions of attackers during shoulder surfing attacks. These results have important implications for shoulder surfing prevention training programs and penetration tests.

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals’ performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Security Analysis of the Consumer Remote SIM Provisioning Protocol

Remote SIM provisioning (RSP) for consumer devices is the protocol specified by the GSM Association for downloading SIM profiles into a secure element in a mobile device. The process is commonly known as eSIM, and it is expected to replace removable SIM cards. The security of the protocol is critical because the profile includes the credentials with which the mobile device will authenticate to the mobile network. In this paper, we present a formal security analysis of the consumer RSP protocol. We model the multi-party protocol in applied pi calculus, define formal security goals, and verify them in ProVerif. The analysis shows that the consumer RSP protocol protects against a network adversary when all the intended participants are honest. However, we also model the protocol in realistic partial compromise scenarios where the adversary controls a legitimate participant or communication channel. The security failures in the partial compromise scenarios reveal weaknesses in the protocol design. The most important observation is that the security of RSP depends unnecessarily on it being encapsulated in a TLS tunnel. Also, the lack of pre-established identifiers means that a compromised download server anywhere in the world or a compromised secure element can be used for attacks against RSP between honest participants. Additionally, the lack of reliable methods for verifying user intent can lead to serious security failures. Based on the findings, we recommend practical improvements to RSP implementations, future versions of the specification, and mobile operator processes to increase the robustness of eSIM security.

Enhanced Security and Privacy via Fragmented Federated Learning.

In federated learning (FL), a set of participants share updates computed on their local data with an aggregator server that combines updates into a global model. However, reconciling accuracy with privacy and security is a challenge to FL. On the one hand, good updates sent by honest participants may reveal their private local information, whereas poisoned updates sent by malicious participants may compromise the model's availability and/or integrity. On the other hand, enhancing privacy via update distortion damages accuracy, whereas doing so via update aggregation damages security because it does not allow the server to filter out individual poisoned updates. To tackle the accuracy-privacy-security conflict, we propose fragmented FL (FFL), in which participants randomly exchange and mix fragments of their updates before sending them to the server. To achieve privacy, we design a lightweight protocol that allows participants to privately exchange and mix encrypted fragments of their updates so that the server can neither obtain individual updates nor link them to their originators. To achieve security, we design a reputation-based defense tailored for FFL that builds trust in participants and their mixed updates based on the quality of the fragments they exchange and the mixed updates they send. Since the exchanged fragments' parameters keep their original coordinates and attackers can be neutralized, the server can correctly reconstruct a global model from the received mixed updates without accuracy loss. Experiments on four real data sets show that FFL can prevent semi-honest servers from mounting privacy attacks, can effectively counter-poisoning attacks, and can keep the accuracy of the global model.

Extending the Security of SPDZ with Fairness

SPDZ refers to a family of protocols for Secure Multi-Party Computation (MPC) that lie at the foundation of very popular software frameworks for MPC, such as SCALE-MAMBA and MP-SPDZ. SPDZ provides good efficiency while guaranteeing security even when all but one of the participants are corrupted. This seemingly optimal property comes at a price: the protocol only offers security with abort, meaning that even a single cheating participant can force the protocol to abort, leaving honest participants with no clue on what the correct output is, or who cheated. This is especially problematic since cheating participants are able to obtain the correct output of the computation, effectively `stealing' it. We propose a *hybrid secure* adaptation to SPDZ, which retains the existing security guarantees, but in case the number of cheating players is less than half of the total, we achieve *fairness*, meaning that either all players obtain the correct output of the computation, or no player does. The `less than half' threshold of corrupted players has been proven to be a tight bound to achieve fairness. Aside from the description of the protocol and its security proof, we also present a proof-of-concept implementation, and evaluate its practical performance, thereby demonstrating that our solution has negligible overhead compared to standard SPDZ in most application scenarios.

Quantum multi-secret sharing scheme with access structures and cheat identification

This work proposes a [Formula: see text]-dimensional quantum multi-secret sharing scheme with a cheat-detection mechanism. The dealer creates multiple secrets and distributes the shares of these secrets using multi-access structures and a monotone span program. The dealer detects the cheating of each participant using the black box’s cheat-detection mechanism. To detect the participants’ deceit, the dealer distributes secret shares’ shadows derived from a randomly invertible matrix [Formula: see text] to the participants, stored in the black box. The black box identifies the participant’s deceitful behavior during the secret recovery phase. Only honest participants authenticated by the black box acquire their secret shares to recover the multiple secrets. After the black box cheating verification, the participants reconstruct the secrets by utilizing the unitary operations and quantum Fourier transform. The proposed protocol is reliable in preventing attacks from eavesdroppers and participants. The scheme’s efficiency is demonstrated in different noise environments: dit-flip noise, [Formula: see text]-phase-flip noise and amplitude-damping noise, indicating its robustness in practical scenarios. The proposed protocol provides greater versatility, security and practicality.

Toward a Blockchain-Based, Reputation-Aware Secure Transactive Energy Market

The rapid expansion of transactive energy has transformed traditional electricity consumers into producers, engaging in local energy trading. In the context of distributed energy transactions, blockchain technology has been increasingly applied to facilitate transaction transparency and reliability. However, due to the challenges in collecting accurate energy transmission data from power lines, most existing studies on the blockchain-based transactive energy market are still vulnerable to security attacks, such as malicious users misreporting energy prices, refusing to pay or refusing to transmit energy. Therefore, based on the co-simulation platform PEMT-CoSim and a blockchain, we establish a blockchain-based, reputation-aware secure transactive energy market (STEM) by introducing a reputation scheme to evaluate the trustworthiness of all prosumers and designing reputation-aware, multi-round double auction and energy transmission algorithms to detect and penalize malicious attacks. Furthermore, we run comprehensive experiments for different use cases. The results show that even with malicious participants, the proposed system can guarantee the interests of the honest participants and improve the robustness and effectiveness of the energy market.

Denials in informal co‐witness conversations do not affect memory for witnessed events

AbstractCampus sexual assault constitutes a frequent crime witnessed by many. Among co‐witnesses such assault is oftentimes denied. We examined how false denials during an informal co‐witness conversation impacted memory for the conversation and witnessed assault. Ninety participants watched a trauma‐analogue video with a co‐witness. The next day, honest control dyads engaged in an honest, informal discussion about the film. In lying dyads one participant falsely denied (internal false denial group) details during this informal discussion with their co‐witness (external denial group). One week later, participants' recognition memory for the video and conversation was tested. We did not replicate denial‐induced forgetting in that participants who falsely denied did not forget details of the previous conversation, relative to honest participants. Moreover, memory for the film was not statistically significantly affected by our manipulation. Thus, informal false denials might not negatively affect memory, despite previous research showing such effects in formal settings.

BPFL: Blockchain-based privacy-preserving federated learning against poisoning attack

In federated learning (FL), multiple clients use local datasets to train models and submit local gradients to the server for aggregation. However, malicious clients may compromise the performance of the model by submitting poisonous gradients. Moreover, the clients do not want to reveal their training models in most application scenarios since their private data may be inferred from them. In addition, most of FL protocols lack an incentive mechanism to supervise participants and cannot punish malicious participants, which is unfair to honest participants. To tackle these problems, we propose a blockchain-based privacy-preserving federated learning against poisoning attack (BPFL). In BPFL, a blockchain-based incentive mechanism is constructed to supervise participants and promptly track malicious behaviors. BPFL can also protect the privacy of aggregated and local model if some participants are malicious, and detect poisonous data by computing cosine similarity between the aggregated gradient and local gradient of the client by using Paillier cryptosystem with threshold decryption. The experiments show that BPFL improves the accuracy of the model from 10% to 75% on CIFAR-10 against poisoning attacks, and therefore BPFL can effectively resist poisoning attacks based on the privacy of local and aggregated models.

Privacy-Preserving k-Nearest Neighbor Classification over Malicious Participants in Outsourced Cloud Environments

In recent years, many companies have chosen to outsource data and other data computation tasks to cloud service providers to reduce costs and increase efficiency. However, there are risks of security and privacy breaches when users outsource data to a cloud environment. Many researchers have proposed schemes based on cryptographic primitives to address these risks under the assumption that the cloud is a semi-honest participant and query users are honest participants. However, in a real-world environment, users’ data privacy and security may be threatened by the presence of malicious participants. Therefore, a novel scheme based on secure multi-party computation is proposed when attackers gain control over both the cloud and a query user in the paper. We prove that our solution can satisfy our goals of security and privacy protection. In addition, our experimental results based on simulated data show feasibility and reliability.

Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications

Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. With a lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to an unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of Things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, a blockchain-based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain; a theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.

An Intelligent Semi-Honest System for Secret Matching against Malicious Adversaries

With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications.

Rethinking selfish mining under pooled mining

Bitcoin’s core security requires honest participants to control at least 51% of the total hash power. However, it has been shown that several techniques can exploit the fair mining in the Bitcoin network. This study focuses on selfish mining, which is based on the idea, ”keeping blocks secret.” Herein, we analyze selfish mining regarding competition between mining pools. We emphasize that mining-related information is shared between mining pools and participants. Based on shared information about selfish mining, we have developed an effective and practical counter strategy.

A secure payment channel rebalancing model for layer-2 blockchain

The concept of cryptocurrencies is prevalently used in the financial world. Some of the currencies like Bitcoin and Ethereum have shown promising outputs. The scalability problem of these cryptocurrencies is an ongoing challenge, solved through many facets. Bitcoin Lightning Network, a Layer-2 solution to Bitcoin Network, is an off-chain crypto transaction platform working on payment channels to transact bi-directionally between two parties. When the maximum payments flow is unidirectional, the individual balances of the parties in the direction of payment get exhausted. It results in the transaction failing for future payments in that direction and lowers the feasibility of network usage. In this paper, we propose a heuristic-based distributed solution by preserving the privacy and anonymity of the parties involved in the rebalancing procedure and improving the fund transfer capability of the channels. The rebalancing mechanism has a global solution that can rebalance to the maximum extent and a local solution using a significantly lower fee. Again the indirect and direct rebalancing schemes balance the imbalanced channel and prevent further transaction failure and closure of channels due to insufficient balance. The resultant method also has an updation in the HTLC message exchange protocol to avoid the Flood and Loot attack that may arise to maintain the safe state of every honest participant in the network.

An incentive system for decentralized DAG-based platforms

Decentralized public platforms are becoming increasingly popular due to a growing number of applications for various areas ofbusiness, finance, and social life. Authorless nodes can easily join such networks without any confirmation, making a transparent system of rewards and punishments crucial for the self-sustainability of public platforms. To achieve this, a system for incentivizing and punishing Workers’ behavior should be tightly harmonized with the corresponding consensus protocol, taking into account all of its features, and facilitating a favorable and supportive environment with equal rights for all participants. The main purpose of re-wards is to incentivize Workers to follow the protocol properly, and to penalize them for any type of misbehavior. The issues of block rewarding and punishing in decentralized networks have been well studied, but the DAG referential structure of the distributed ledger forces us to design methods that are more relevant. Since referential structures cannot be reliably validated due to the fact that they are built on the basis of the instantaneous visibility of blocks by a certain node, we propose to set rewards for blocksin the DAG network based on the degree of confidence of topological structures. In doing so, all honest nodes make common decisions based only on information recorded into the ledger, without overloading the network with additional interactions, since such data are al-ways identical and available.The main goal of this work is to design a fair distribution of rewards among honest Workers and estab-lish values for penalties for faulty ones, to ensure the general economic equilibrium of the Waterfall platform. The proposedap-proach has a flexible and transparent architecture that allows for its use for a wide range of PoS-based consensus protocols. The core principles are that Workers' rewards depend on the importance of the conducted work for block producing and achieving consensusand their penalties must not be less than the potential profit from possible attacks. The incentivizing system can facilitate protection from various kinds of attacks, namely, so-called Nothing-at-stake, Rich-get-richer, Sybil, and Splitting attacks, and from some specif-ic threats related to a DAG structure.

Continuous Variable Quantum Secret Sharing with Security Enhancement in Practical Quantum Communications

Quantum communications can be conveniently implemented by two participants, but quantum secret key sharing (QSS) through multi-participant communication seems difficult in practice. In this paper, we propose a multi-participant QSS scheme with the local local oscillator (LLO) in continuous variable (CV) quantum communications. It allows an honest participant called a dealer to share a secret key with the others, making it possible to establish a secret key if and only if all participants gather together. The LLO scheme, which eliminates the need for the local oscillator (LO) to be propagated in insecure channels, can be used to avoid the potential LO attack in the traditional CVQSS scheme. Numerical simulation shows that the performance of the LLO-based CVQSS can be improved in terms of the maximal transmission distance even if it is performed with the partially trusted noise.

Registered report: The effects of incentivized lies on memory

AbstractIn the current experiment, we examined the effects of self‐generated deceptive behavior on memory. Participants (n = 230) were randomly assigned to a “strong‐incentive to cheat” or “weak‐incentive to cheat” condition and played the adapted Sequential Dyadic Die‐Rolling paradigm. Participants in the “strong‐incentive to cheat” condition were incentivized to lie to avoid a financial penalty (i.e., punishment). Participants in the “weak‐incentive to cheat” could also choose to lie but the outcome was a prosocial one (i.e., benefit for unknown co‐participant). Two‐days later, memory for the die‐rolling event was assessed. A similar number of participants lied to avoid punishment as for prosocial reasons. Interestingly, we did not find evidence for unethical amnesia. However, participants who engaged in deceptive behavior, irrespective of their motives, produced more memory errors than honest participants. Although our results suggest that engaging in deceptive behavior does not lead to motivated forgetting, it can lead to memory errors.

False denials increase false memories for trauma-related discussions

ABSTRACT False denials are sometimes used to cope with traumatic experiences. We examined whether false denials can affect true and false memory production for a traumatic event and conversations surrounding the trauma. One hundred and twenty-six participants watched a trauma analogue video of a car crash before being randomly asked in a discussion with the experimenter to (1) respond honestly or (2) falsely deny that certain details happened in the video. After one week, all participants received misinformation about the discussion with the experimenter and the car crash. Finally, all participants were instructed to respond truthfully in a source memory task. Participants who falsely denied information during the first session were statistically significantly more prone than honest participants to omit details they denied and to report misinformation about what was discussed in the first session. Our work suggests that false denials of a traumatic experience might lead to both forgetting and increased false memory levels for earlier conversations about the event.

Collusion-Resistant Worker Set Selection for Transparent and Verifiable Voting.

Collusion occurs when multiple malicious participants of a distributed protocol work together to sabotage or spy on honest participants. decentralized protocols often rely on a subset of participants called workers for critical operations. Collusion between workers can be particularly harmful to the security of the protocol. We propose two protocols that select a subset of workers from the set of participants such that the probability of the workers colluding together is minimized. Our first solution is a decentralized protocol that randomly selects workers in a verifiable manner without any trusted entities. The second solution is an algorithm that uses a social graph of participants and community detection to select workers that are socially distant in order to reduce the probability of collusion. We present our solutions in the context of a decentralized voting protocol proposed by Schiedermeier et al. (A transparent referendum protocol with immutable proceedings and verifiable outcome for trustless networks, Springer, Cham, 2019) that guarantees transparency and verifiability. Enabling collusion-resistance in order to ensure democratic voting is clearly of paramount importance thus the voting protocol provides a suitable use case for our solutions.