Collusion Attacks Research Articles

Mnemosyne: Privacy-Preserving Ride Matching With Collusion-Resistant Driver Exclusion

Ride-Hailing Service (RHS) has drawn plenty of attention as it provides transportation convenience for riders and financial incentives for drivers. Despite these benefits, riders risk the exposure of sensitive location data during ride requesting to an untrusted Ride-Hailing Service Provider (RHSP). Our motivation arises from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repetitive matching, i.e., the same driver is repetitively assigned to the same rider</i> . Meanwhile, we introduce a driver exclusion function to protect riders' location privacy. Existing work on privacy-preserving RHS overlooks this function. While Secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> Nearest Neighbor (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN) facilitates efficient matching, the state-of-the-art neglects a collusion attack. To solve this problem, we formally define repetitive matching and strong location privacy, and propose Mnemosyne: privacy-preserving ride matching with collusion-resistant driver exclusion. We extend the simple integration of equality checking and item exclusion to a dynamic integration. We concatenate each prefix of an acceptable identity range to each location code when generating a ride request, i.e., secure mix index. We process each prefix of the driver identity to generate a ride response, i.e., a mix token. We build an indistinguishable Bloom-filter as an index to query the token. When matching riders with drivers, the colluding parties cannot distinguish identity prefixes from location codes. We build a prototype of Mnemosyne based on servers, smartphones, and a real-world dataset. Experimental results demonstrate that Mnemosyne outperforms existing work regarding strong location privacy and computational costs.

A dual auditing protocol for fine-grained access control in the edge-cloud-based smart home

Devices and edges of the Internet of Things (IoT) are working together to broaden the sensing capability of the IoT. The protection of personal sensory information privacy in the smart home has gotten more attention. To enable secure data sharing under the scenario of the smart home, attribute-based encryption (ABE) has been applied widely. As a general rule, the sophisticated calculation of ABE is difficult under resource-constrained devices. The appearance of edge computing alleviates this dilemma, where an edge server (ES) is responsible for computing all heavy cryptographic operations and uploading massive device data to a cloud. Nevertheless, the ES and the cloud may be attacked artificially in reality. Accordingly, to check the correctness of the ES data and the integrity of the cloud data, we design a lightweight dual auditing scheme, where the first adopts the aggregation of data blocks and signatures to check the integrity of the cloud data, and the second constructs an efficient Zero-Knowledge (ZK) proof to verify the correctness of the ES data in a lightweight way. To prevent collusion attacks, the legitimacy of the data user is authenticated by a smart contract. The simulation experiment shows that our multiple auditing scheme is relatively efficient.

Research on smart city data encryption and communication efficiency improvement under federated learning framework

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95% for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87%and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization.

Decentralized privacy-preserving truth discovery for crowd sensing

Truth discovery is an efficient technique for tackling data conflict problems in crowd sensing for distributed data collection. As the sensory data to be collected may include sensitive information about users, privacy-preserving truth discovery has attracted significant attention in recent years. Most existing studies apply a centralized architecture based on a cryptographic system, which may be vulnerable to single-point attacks and also has a very high computational cost. In this paper, we propose DPriTD, a decentralized privacy-preserving framework for truth discovery in crowd sensing. The proposed approach leverages the additively homomorphic property of Shamir's Secret Sharing scheme to protect user's privacy. DPriTD provides a strict privacy guarantee for crowd sensing applications. Because each sensitive data point, considered to be a secret, is split into a batch of shares, and the secret cannot be recovered unless a sufficient number of shares are aggregated, DPriTD achieves effective truth discovery while protecting sensitive data from collusion attacks. Furthermore, DPriTD is independent of a centralized server and can perform reliably when not all participants are online in real time. It thus enhances the robustness of a crowd sensing system. Extensive experiments conducted on real-world datasets demonstrate the high performance of our method compared with existing mechanisms.

Resilient consensus of high-order networks against collusive attacks

This paper investigates the resilient consensus problem of high-order networks under malicious attacks. The main difficulty lies in the collusion among attacks against the attack detection and isolation. To overcome such difficulty, a novel attack isolation strategy is developed, based on which, a control algorithm is provided to achieve resilient consensus. A new notion of (r,s)-isolability is introduced to present sufficient conditions for attack isolation. Finally, the effectiveness of the proposed approach is demonstrated by simulation studies.

Improved Certificateless Aggregate Signature Scheme Against Collusion Attacks for VANETs

Vehicle ad-hoc networks (VANETs) can offer numerous benefits, but also face many privacy and security issues. There exist the certificate management burden and key escrow issue in traditional public key infrastructure (PKI)-based and identity (ID)-based privacy-preserving authentication schemes, respectively. In addition, the security of some existing cryptographic schemes depends on strong assumptions about the ideal tamper-proof devices. To address these issues, a certificateless aggregate signature (CLAS) scheme for VANETs has been proposed, which was claimed provably unforgeable. However, we found that this scheme cannot resist collusion attacks, which means the equivalent validity of the aggregate signature would be broken. Therefore, this article proposes an improved CLAS scheme against collusion attacks for VANETs (CA-CLAS). Security and performance analyses show that the proposed CA-CLAS scheme not only offers privacy and security guarantees for VANETs, but also improves efficiency compared to existing authentication schemes.

Attacks and Countermeasures on Privacy-Preserving Biometric Authentication Schemes

Based on the Threshold Predicate Encryption (TPE), the biometric authentication scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PassBio</i> aims to correctly authenticate genuine end-users without leaking their biometric privacy information. However, this article proposes two impersonation attacks to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PassBio</i> by merely sending very few query messages. Specifically, an attacker is able to cheat the authentication server with probability 50% by sending the server a random query, or almost 100% by sending the server a collusion of old genuine queries, without being identified. Moreover, in order to defeat the impersonation attacks, this article presents a Verifiable Threshold Predicate Encryption (VTPE) scheme which includes three components: (1) a multi-segment TPE for reducing the computational cost and communication overhead significantly; (2) a segment-wise watermarking for defeating the random attacks; and (3) a challenge-response mechanism for defeating the replay and collusion attacks. In addition, the watermarking also creates a secure channel between the querying user and the server. The experiments on both simulated feature vectors and real face images demonstrate that the present attacks and countermeasures are effective and efficient.

Contract-Theory-Based Incentive Mechanism for Federated Learning in Health CrowdSensing

Federated learning (FL) provides an effective solution for multiparty data processing under privacy preserving, and becomes a good choice for crowd intelligence extraction in Health CrowdSensing. The quality of the local model submitted by the data holder determines the quality of the global model in FL, and the quality of the local model depends on the data quantity, data quality, and computing power of the data holder. However, in the process of model training, the data holder will inevitably spend the cost of communication and local model training, and higher quality data acquisition and higher quality local model training require higher cost. Therefore, how to motivate data holders with a large amount of high-quality data and computing power to participate in FL has become an urgent problem to be solved. This article transforms the problem of motivating data holders into an optimization problem of utility from the perspective of maximizing the utility of the data holder, establishes the incentive mechanism based on the Contract Theory, and proves that the optimal strategy set of the data holders reaches Nash Equilibrium. A large number of experiments based on public data sets of UCI and MNIST verify that the incentive mechanism can make the baseline algorithm converge faster, while resisting malicious behaviors, such as free-riding and collusive attacks. Furthermore, the data holder with a large amount of high-quality data and computing power can obtain higher revenue.

LIGHT WEIGHT SECURITY ALGORITHM FOR LOW POWER IOT DEVICE

Many IOT devices(mainly networks of IOT devices or networks that are deployed to monitor calamity situations) are deployed in an arbitrary and unplanned fashion. For any sensor in such a network can end up being adjacent node to any other IOT node in the network. To Establish a secure communication between every pair of adjacent IOTs node in such a network, each sensor node x in the network needs to store n -1 number of symmetric keys that sensor node x shares with all the other sensor nodes, where n is the number of sensor nodes in the present network. This memory storage requirement of the keying protocol is various, especially when n is large and the available storage in each sensor node is modest. Previous efforts to redesign this keying protocol and reduce the number of keys to be stored in each sensor node have produced protocols that are vulnerable to impersonation, eavesdropping, and collusion attacks. In this paper, we present a fully secure protocol.

SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning

With the massive amounts of data generated by industrial Internet of Things (IIoT) devices at all moments, federated learning (FL) enables these distributed distrusted devices to collaborate to build machine learning model while maintaining data privacy. However, malicious participants still launch malicious attacks against the security vulnerabilities during model aggregation. This article is the first to propose Sybil-based collusion attacks (SCA) in the IIoT-FL system for the vulnerabilities mentioned above. The malicious participants use label flipping attacks to complete local poisoning training. Meanwhile, they can virtualize multiple Sybil nodes to make the local poisoning models aggregated with the greatest possibility during aggregation. They focus on making the joint model misclassify the selected attack class samples during the testing phase, while other nonattack classes kept the main task accuracy similar to the nonpoisoned state. Exhaustive experimental analysis demonstrates that our SCA has a superior performance on multiple aspects than the state-of-the-art.

A efficient and robust privacy-preserving framework for cross-device federated learning

To ensure no private information is leaked in the aggregation phase in federated learning (FL), many frameworks use homomorphic encryption (HE) to mask local model updates. However, the heavy overheads of these frameworks make them unsuitable for cross-device FL, where the clients are a huge number of mobile and edge devices with limited computing resources. Even worse, some of them also fail to manage the dynamic changes of clients. To overcome these shortcomings, we propose a threshold multi-key HE scheme tMK-CKKS and design an efficient and robust privacy-preserving FL framework. Robustness means that our framework allows clients to join in or drop out during the training process. Besides, because our tMK-CKKS scheme can pack multiple messages in a single ciphertext, our framework significantly reduces the computation and communication overhead. Moreover, the threshold mechanism in tMK-CKKS ensures that our framework can resist collusion attacks between the server and no more than t (threshold value) curious internal clients. Finally, we implement our framework in FedML and conduct extensive experiments to evaluate our framework. Utility evaluations on 6 benchmark datasets show that our framework can protect privacy without sacrificing the model accuracy. Efficiency evaluations on 4 typical deep learning models demonstrate that: our framework can speed up the computation by at least 1.21times over xMK-CKKS-based framework, 15.84times over Batchcrypt-based framework, and 20.30times over CRT-Paillier-based framework. Our framework can reduce the communication burden by at least 8.61 MB over Batchcrypt-based framework, 35.36 MB over xMK-CKKS-based framework and 42.58 MB over CRT-Paillier-based framework. The advantages in both computation and communication expand with the size of deep learning models.

Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee

With the massive deployment of the Internet of Things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing FedL schemes either take expensive computation costs (e.g., public-key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving FedL scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret-sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

Edge-Enabled: A Scalable and Decentralized Data Aggregation Scheme for IoT

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n\sqrt{n})$</tex-math></inline-formula> with constant rounds, as opposed to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n\log n)$</tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.

Privacy-Preserving Federated Learning for Internet of Medical Things under Edge Computing.

Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT) and industrial control UAV clusters, which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force when edge intelligent devices, edge gateways and clouds complete the task unloading, as well as scheduling and coordination issues. Federated learning allows all training devices to complete training at the same time, which greatly improves training efficiency. However, traditional federated learning will expose patient's privacy information of the training set. Due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the central servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than federated learning for secure multiparty computing frameworks based on homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the \textcolor{blue}{integrity and consistency} of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40% faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.

A quantum threshold signature protocol based on mutual unbiased basis

For the existing quantum threshold signature protocols based on entangled state, it is difficult to prepare quantum resources. Meanwhile, for the quantum threshold signature protocols based on single-particle sequence, it has disadvantages of high computation and communication consumptions. To remedy these shortcomings, in this paper, we proposed a quantum threshold signature protocol based on mutual unbiased basis (MUB). In initialization stage, the message owner Alice prepares a quantum sequence [Formula: see text] with length of [Formula: see text], each single particle in [Formula: see text] is chosen from MUBs. The signature key is generated by Alice using threshold secret-sharing distribution algorithm, the verification key is generated by [Formula: see text] signers via threshold secret-sharing recovery algorithm. In signature generation phase, the signers execute the signature algorithm sequentially until the last signer and then obtain the final signature, next send it to verifier. In signature verification phase, the verifier Ver verifies the original message and the final signature using the verification key. Security analysis shows that the proposed protocol can resist interception-forgery attack, collusion attack and denial attack. Performance analysis shows that the proposed protocol is easier to prepare quantum resources than similar protocols, and has lower communication consumption, especially when the number of signers [Formula: see text] and increase gradually. Finally, the correctness of the proposed protocol is verified by simulation experiment on quantum cloud platform.

Privacy-preserving association rule mining via multi-key fully homomorphic encryption

Association rule mining is an efficient method to mine the association relationships between different items from large transaction databases, but is vulnerable to privacy leakage as operates over users’ sensitive data directly. Privacy-preserving association rule mining has emerged to protect users’ privacy during rule mining. Unfortunately, existing privacy-preserving association rule mining schemes suffer from high overhead, fail to support multiple users, and are challenging to prevent collusion attacks between twin-server. To this end, in this paper, we propose a privacy-preserving association rule mining solution via multi-key fully homomorphic encryption over the torus (MKTFHE), which efficiently supports multiple users through a single server only. Specifically, we first construct some multi-key homomorphic gates based on MKTFHE. Then, we designed a series of privacy-preserving computational protocols based on multi-key homomorphic gates. Finally, we build a privacy-preserving association rule mining system with a single cloud server to support multiple users. Moreover, privacy analysis and performance evaluation demonstrate our proposal is efficient and feasible. In contrast to existing solutions, the proposed scheme outperforms encryption and communication, saving approximately 8.5% running time.

IAFDroid: Demystifying Collusion Attacks in Android Ecosystem via Precise Inter-App Analysis

Inter-app communication mechanism allows app developers to improve Android apps’ usability and provide users with rich functions via interacting with exposed components or performing data sharing. However, this mechanism may be leveraged by malicious developers or unintentionally misused by inexperienced developers. For end users, this type of attack may cause privacy breaches or remote controls of personal phone, which have a great threat of the user’s data security. The key to analyzing whether there is a collusion attack is to analyze the existence of communication channels between apps. The existing static analysis tools have done some work in this area, but the comprehensiveness of the analysis is insufficient, and such research lacks a unified test standard. In this paper, we present collusion attacks using more concealed inter-app communication channels, which can bypass existing security detection mechanisms. To defend against the new attacks, we design IAFDroid, an analysis framework that combines static and taint analysis. By examining 20K real-world apps, IAFDroid found that 94.4% of the most exposed components of Android may be leveraged to perform collusion attacks. Furthermore, the evaluation showed that the feature set extracted by IAFDroid could be used to promote the accuracy of Android malware detection. We contribute a more comprehensive benchmark for IAC analysis, IACBench, which includes the new attacks we propose. To facilitate follow-up studies, we open-sourced IAFDroid and IACBench based on the GPL agreement.

Blockchain-based multi-authority revocable data sharing scheme in smart grid.

In view of the problems of inefficient data encryption, non-support of malicious user revocation and data integrity checking in current smart grid data sharing schemes, this paper proposes a blockchain-based multi-authority revocable data sharing scheme in the smart grid. Using online/offline encryption technology with hybrid encryption technology enhances the encryption performance for the data owner. The use of user binary tree technology enables the traceability and revocability of malicious users. The introduction of multiple attribute authorization authorities eliminates the threat of collusive attacks that exist in traditional data-sharing schemes. In addition, the semi-honest problem of third-party servers is solved by uploading data verification credentials to the blockchain. The security analysis results show that the scheme can resist selective plaintext attacks and collusion attacks. The performance analysis results show that the proposed scheme has lower computational overhead and better functionality than similar schemes, which is suitable for secure data sharing in smart grids.

Blockchain-based Privacy-Preserving Group Data Auditing with Secure User Revocation

Progress in cloud computing makes group data sharing in outsourced storage a reality. People join in group and share data with each other, making team work more convenient. This new application scenario also faces data security threats, even more complex. When a user quit its group, remaining data block signatures must be re-signed to ensure security. Some researchers noticed this problem and proposed a few works to relieve computing overhead on user side. However, considering the privacy and security need of group auditing, there still lacks a comprehensive solution to implement secure group user revocation, supporting identity privacy preserving and collusion attack resistance. Aiming at this target, we construct a concrete scheme based on ring signature and smart contracts. We introduce linkable ring signature to build a kind of novel meta data for integrity proof enabling anonymous verification. And the new meta data supports secure revocation. Meanwhile, smart contracts are using for resisting possible collusion attack and malicious re-signing computation. Under the combined effectiveness of both signature method and blockchain smart contracts, our proposal supports reliable user revocation and signature re-signing, without revealing any user identity in the whole process. Security and performance analysis compared with previous works prove that the proposed scheme is feasible and efficient.

Blockchain-Based Privacy-Preserving Public Auditing for Group Shared Data

Cloud storage has been widely used to team work or cooperation development. Data owners set up groups, generating and uploading their data to cloud storage, while other users in the groups download and make use of it, which is called group data sharing. As all kinds of cloud service, data group sharing also suffers from hardware/software failures and human errors. Provable Data Possession (PDP) schemes are proposed to check the integrity of data stored in cloud without downloading. However, there are still some unmet needs lying in auditing group shared data. Researchers propose four issues necessary for a secure group shared data auditing: public verification, identity privacy, collusion attack resistance and traceability. However, none of the published work has succeeded in achieving all of these properties so far. In this paper, we propose a novel blockchain-based ring signature PDP scheme for group shared data, with an instance deployed on a cloud server. We design a linkable ring signature method called Linkable Homomorphic Authenticable Ring Signature (LHARS) to implement public anonymous auditing for group data. We also build smart contracts to resist collusion attack in group auditing. The security analysis and performance evaluation prove that our scheme is both secure and efficient.