User Collusion Research Articles

Identification and Evolutionary Analysis of User Collusion Behavior in Blockchain Online Social Media

Blockchain technology has given rise to a series of new blockchain online social medias (BOSMs), of which Steemit is representative. Such communities are based on a token reward system and attempt to engross users in the knowledge activities of the community through knowledge payment. Studies have found that the reward system of such communities has been abused (e.g., collusion for profit), but few studies have performed an in-depth analysis for this phenomenon. Consequently, real data for Steemit are used as a case study herein to examine the collusion of users in BOSMs. Two user collusion behaviors (group-voting and vote-buying) are defined and measured. On this basis, an identification and evolutionary survival analysis of the two collusion behaviors are conducted for colluding users and colluding groups, and the behavior patterns of user collusion under the token system are deconstructed. The results of this study improve stakeholders’ understanding of user participation behavior in new online communities, and serve as a reference for decision-making in community governance and token design.

An efficient multi-data owner cooperative resource sharing scheme against key regeneration in edge computing

In cloud storage, attribute-based keyword searchable encryption realizes multi-user fine-grained authorization access control and outsourcing data sharing. However, (1) resisting key regeneration is an urgent problem to be solved in practical applications of attribute-based keyword search; (2) the general attribute-based keyword search scheme ignores the application scenario where multiple data owners jointly manage files. In addition, the interaction of data between intelligent terminals and cloud servers has become more frequent, leading to issues such as central load, transmission efficiency, and data privacy protection in cloud computing. To address the above issues, we propose an attribute-based searchable encryption scheme with the multi-data owner based on edge computing. In this scheme, the Pedersen (k,n) secret sharing protocol and the Reciprocal protocol are used to realize the common management and sharing of data by the multi-data owner to enhance data security. Secondly, the attribute set and identity information of each user are embedded into the private key to prevent key regeneration and resist user collusion attacks. Thirdly, edge computing is introduced, and part of the storage and decryption work is outsourced to edge nodes to reduce the computing load of users and data transmission bandwidth. Security proof and performance analysis show that the proposed scheme has strong security and practicability in multi-user sharing scenarios such as Online Education with multi-data owners.

On the Fundamental Limits of Device-to-Device Private Caching Under Uncoded Cache Placement and User Collusion

In the coded caching problem, as originally formulated by Maddah-Ali and Niesen, a server communicates via a noiseless shared broadcast link to multiple users that have local storage capability. In order for a user to decode its demanded file from the coded multicast transmission, the demands of all the users must be globally known, which may violate the privacy of the users. To overcome this privacy problem, Wan and Caire recently proposed several schemes that attain coded multicasting gain while simultaneously guarantee information theoretic privacy of the users’ demands. In Device-to-Device (D2D) networks, the demand privacy problem is further exacerbated by the fact that each user is also a transmitter, which appears to be needing the knowledge of the files demanded by the remaining users in order to form its coded multicast transmission. This paper shows how to solve this seemingly infeasible problem. The main contribution of this paper is the development of new achievable and converse bounds for D2D coded caching that are to within a constant factor of one another when privacy of the users’ demands must be guaranteed even in the presence of colluding users (i.e., when some users share cached contents and demanded file indices). First, a D2D private caching scheme is proposed, whose key feature is the addition of virtual users in the system in order to “hide” the demands of the real users. By comparing the achievable D2D private load with an existing converse bound for the shared-link model without demand privacy constraint, the proposed scheme is shown to be order optimal, except for the very low memory size regime with more files than users. Second, in order to shed light into the open parameter regime, a new achievable scheme and a new converse bound under the constraint of uncoded cache placement (i.e., when each user stores directly a subset of the bits of the library) are developed for the case of two users, and shown to be to within a constant factor of one another for all system parameters. Finally, the two-user converse bound is extended to any number of users by a cut-set type argument. With this new converse bound, the virtual users scheme is shown to be order optimal in all parameter regimes under the constraint of uncoded cache placement and user collusion.

Terminal independent security token derivation scheme for ultra-dense IoT networks

The Fifth Generation (5G) networks deploy base station ultra-densification to boost data rates, capacities, reliability, energy efficiency as well as the reduction of communication latencies. To increase quality of service as well as quality of experience, a large number of Internet of Things (IoT) communications are relayed over 5G networks. For enhanced pervasive computing, most of the devices in 5G-IoT networks are continuously connected to the network, exchanging massive and sensitive data. Therefore, there is need to protect these networks from both privacy and security attacks. As a result, many security protocols have been presented in literature. Unfortunately, IoT devices are heterogeneous in nature with diverse communication and security architectures. These issues render privacy and security protection extremely challenging. Consequently, majority of the conventional protocols fail to fully address privacy and security issues in 5G-IoT networks. Particularly, user collusion, de-synchronization and side-channeling attacks are ignored in most of the security protocols. On the other hand, some of the developed protocols achieve salient security but at extremely high computation, storage and communication complexities. In this paper, an elliptic curve and biometric based security token derivation scheme is presented. Formal security analysis using Burrows–Abadi–Needham (BAN) logic shows the negotiation of a session key between the communicating parties. On the other hand, informal security analysis shows that this scheme is secure under all the Canetti- Krawczyk (CK) threat model assumptions. In terms of efficiency, the comparative performance evaluation carried out shows that this protocol has the least communication and computation complexities among other related protocols.

Secure Online Examination with Biometric Authentication and Blockchain-Based Framework

E-learning has been carried out all over the world and then online examinations have become an important means to check learning effect during the outbreak of COVID-19. Participant authenticity, data integrity, and access control are the assurance to online examination. The existing online examination schemes cannot provide the protection of biometric features and fine-grained access control. Particularly, they did not discuss how to resolve some disputes among students, teachers, and a platform in a fair and reasonable way. We propose a novel biometric authentication and blockchain-based online examination scheme. The examination data are encrypted to store in a distributed system, which can be obtained only if the user satisfies decryption policy. And the pieces of evidence are recorded in a blockchain network which is jointly established by some credible institutions. Unlike other examination authentication systems, face templates in our scheme are protected using a fuzzy vault and a cryptographic method. Furthermore, educational administrative department can determine who the real initiator of malicious behavior is when a dispute arises using a dispute determination protocol. Analysis shows that no central authority is required in our scheme; the collusion of multiple users cannot obtain more data; even if the authorities compromise, biometric features of each user will not be leaked. Therefore, in terms of privacy-preserving biometric templates, fine-grained access, and dispute resolution, it is superior to the existing schemes.

Blockchain-Based Access Control Scheme for Secure Shared Personal Health Records over Decentralised Storage.

Blockchain technology provides a tremendous opportunity to transform current personal health record (PHR) systems into a decentralised network infrastructure. However, such technology possesses some drawbacks, such as issues in privacy and storage capacity. Given its transparency and decentralised features, medical data are visible to everyone on the network and are inappropriate for certain medical applications. By contrast, storing vast medical data, such as patient medical history, laboratory tests, X-rays, and MRIs, significantly affect the repository storage of blockchain. This study bridges the gap between PHRs and blockchain technology by offloading the vast medical data into the InterPlanetary File System (IPFS) storage and establishing an enforced cryptographic authorisation and access control scheme for outsourced encrypted medical data. The access control scheme is constructed on the basis of the new lightweight cryptographic concept named smart contract-based attribute-based searchable encryption (SC-ABSE). This newly cryptographic primitive is developed by extending ciphertext-policy attribute-based encryption (CP-ABE) and searchable symmetric encryption (SSE) and by leveraging the technology of smart contracts to achieve the following: (1) efficient and secure fine-grained access control of outsourced encrypted data, (2) confidentiality of data by eliminating trusted private key generators, and (3) multikeyword searchable mechanism. Based on decisional bilinear Diffie–Hellman hardness assumptions (DBDH) and discrete logarithm (DL) problems, the rigorous security indistinguishability analysis indicates that SC-ABSE is secure against the chosen-keyword attack (CKA) and keyword secrecy (KS) in the standard model. In addition, user collusion attacks are prevented, and the tamper-proof resistance of data is ensured. Furthermore, security validation is verified by simulating a formal verification scenario using Automated Validation of Internet Security Protocols and Applications (AVISPA), thereby unveiling that SC-ABSE is resistant to man-in-the-middle (MIM) and replay attacks. The experimental analysis utilised real-world datasets to demonstrate the efficiency and utility of SC-ABSE in terms of computation overhead, storage cost and communication overhead. The proposed scheme is also designed and developed to evaluate throughput and latency transactions using a standard benchmark tool known as Caliper. Lastly, simulation results show that SC-ABSE has high throughput and low latency, with an ultimate increase in network life compared with traditional healthcare systems.

A Double-Blind Anonymous Evaluation-Based Trust Model in Cloud Computing Environments

In the last ten years, cloud services provided many applications in various areas. Most of them are hosted in a heterogeneous distributed large-scale cloud computing environment and face inherent uncertainty, unreliability, and malicious attacks that trouble both service users and providers. To solve the problems of malicious attacks (including solo and collusion deception ones) in a public cloud computing environment, we for the first time propose a double-blind anonymous evaluation-based trust model. Based on it, cloud service providers and users are anonymously matched according to user requirements. It can be used to effectively handle some malicious attacks that intend to distort trust evaluations. Providers may secretly hide gain-sharing information into service results and send the results to users to ask for higher trust evaluations than their deserved ones. This paper proposes to adopt checking nodes to help detect such behavior. It then conducts gain–loss analysis for providers who intend to perform provider–user collusion deception. The proposed trust model can be used to effectively help one recognize collusion deception behavior and allow policy-makers to set suitable loss to punish malicious providers. Consequently, provider-initiated collusion deception behavior can be greatly discouraged in public cloud computing systems. Simulation results show that the proposed method outperform two updated methods, i.e., one based on fail-stop signature and another based on fuzzy mathematics in terms of malicious node detection ratio and speed.

Secure Efficient Revocable Large Universe Multi-Authority Attribute-Based Encryption for Cloud-Aided IoT

With the help of cloud computing, the ubiquitous and diversified Internet of things (IoT) has greatly improved human society. Revocable multi-authority attribute-based encryption (MA-ABE) is considered a promising technique to solve the security challenges on data access control in the dynamic IoT since it can achieve dynamic access control over the encrypted data. However, on the one hand, the existing revocable large universe MA-ABE suffers the collusion attack launched by revoked users and non-revoked users. On the other hand, the user collusion avoidance revocable MA-ABE schemes do not support large attributes (or users) universe, i.e. the flexible number of attributes (or users). In this article, the author proposes an efficient revocable large universe MA-ABE based on prime order bilinear groups. The proposed scheme supports user-attribute revocation, i.e., the revoked user only loses one or more attributes, and she/he can access the data so long as her/his remaining attributes satisfy the access policy. It is static security in the random oracle model under the q-DPBDHE2 assumption. Moreover, it is secure against the collusion attack launched by revoked users and non-revoked users. Meanwhile, it meets the requirements of forward and backward security. The limited-resource users can choose outsourcing decryption to save resources. The performance analysis results indicate that it is suitable for large-scale cross-domain collaboration in the dynamic cloud-aided IoT.

Collaborative Ehealth Privacy and Security: An Access Control With Attribute Revocation Based on OBDD Access Structure.

The digitization of health records due to technological developments has paved the way for patients to be collaboratively treated by different healthcare institutions. In collaborative ehealth systems, a patient's health data is stored remotely in the cloud for sharing with different healthcare service providers. However, the use of third parties for storage exposes the data to several privacy and security violation threats. Ciphertext policy attribute-based encryption (CP-ABE) which provides a fine-grained access control is a promising solution to privacy and security issues in the cloud environment and as a result, it has been widely studied for secure sharing of health data in cloud-based ehealth systems. Addressing the aspects of expressiveness, efficiency, user collusion resistance and attribute/user revocation in CP-ABE have been at the forefront of these studies. Thus, in this article, we proposed a novel expressive, efficient and collusion-resistant access control scheme with immediate attribute/user revocation for secure sharing of health data in collaborative ehealth systems. The proposed scheme additionally achieves forward and backward security. To realize these features, our access control is based on the ordered binary decision diagram (OBDD) access structure and it binds the user keys to the user identities. Security and performance analysis show that our proposed scheme is secure, expressive and efficient.

A Privacy-Preserving Authentication, Authorization, and Key Agreement Scheme for Wireless Sensor Networks in 5G-Integrated Internet of Things

Wireless sensor networks (WSNs) have played an important role in the Internet of Things (IoT), and the 5G network is being considered as a major candidate for IoT's communication network with the advent of 5G commercialization. The potential of integrating WSNs and 5G in the IoT is expected to allow IoT to penetrate deeply into our daily lives and to provide various services that are convenient, but at the same time, it also brings new security threats. From this aspect, user authentication and key agreement are essential for secure end-to-end communication. As IoT devices, including sensors, collect and process more and more personal information, both anonymous authentication and authorization are also required to protect the privacy and to prevent anyone without privileges from accessing private data. Recently, Adavoudi-Jolfaei et al. proposed an anonymous three-factor authentication and access control scheme for real-time applications in WSNs. However, we found that this scheme does not provide sensor-node anonymity and suffers from user collusion and desynchronization attacks. In this paper, we introduce a system architecture by considering the integration of WSNs and 5G for IoT. Based on a cryptanalysis of Adavoudi-Jolfaei et al.'s scheme and the system architecture, we propose an elliptic curve cryptography (ECC)-based privacy-preserving authentication, authorization, and key agreement scheme for WSNs in 5G-integrated IoT. We conduct a formal and informal security analysis in order to demonstrate that the proposed scheme withstands various security attacks and guarantees all desired security features, overcoming the drawbacks of Adavoudi-Jolfaei et al.'s scheme. Finally, a performance and comparative analysis with the related schemes indicate that the proposed scheme is both efficient and more secure.

Privacy Protection of Social Networks Based on Classified Attribute Encryption

With the rapid development of social networks, privacy has also attracted attention. Based on this problem, a privacy protection scheme for social networks based on classified attribute encryption (PPSSN) is proposed for the data owner and attribute management server to manage user permissions; the approach reduces data owner overhead and also avoids use of a property management server to limit access user collusion attacks. To balance the privacy and security of data publication, this scheme classifies users and designs access control for different users and different privileges. In addition, this paper also introduces a good friend data cache mechanism to improve and optimize the original scheme to reduce the cost of decryption. The efficiency and system overhead of the proposed scheme are compared and analyzed based on experiments. The experiments show that the proposed scheme improves query efficiency, reduces system cost, and enhances privacy security.

Correction to “Improving Privacy and Security in Decentralizing Multi-Authority Attribute-Based Encryption in Cloud Computing”

In 2018, Yang et al. proposed a decentralized multi-authority attribute-based encryption scheme for cloud computing applications and proved its security using the dual system encryption technique. In this comment, we show that Yang et al. ’s scheme does not achieve encryption one-wayness under the key-only attack and the user collusion attack, respectively. In the key-only attack, with the knowledge of public parameters only, an adversary can impersonate the attribute authorities to forge user attribute secret keys. In the user collusion attack, malicious users can collude by sharing their secret keys to unauthorizedly decrypt a ciphertext. In order to fix the scheme, we suggest adopting a pairing-based proof of knowledge protocol and the decryption algorithm from Lewko and Water’s ABE scheme.

Enhanced Privacy Preserving Group Nearest Neighbor Search

Group $k$ k -nearest neighbor ( $k$ k GNN) search allows a group of $n$ n mobile users to jointly retrieve $k$ k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving $k$ k GNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving $k$ k GNN search in the full user collusion environment, where any $n-1$ n - 1 users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider $k$ k GNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., $k$ k GNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.

Efficient revocable CP-ABE for big data access control in cloud computing

Due to huge volume of big data, cloud is a better choice to store big data. Since the cloud is not trustworthy, privacy and access control is a big concern. Ciphertext policy attribute-based encryption (CP-ABE) is a promising technique to enable both privacy and access control in the cloud. However, directly applying CP-ABE scheme for big data in the cloud is a challenging task because of revocation. Existing CP-ABE with revocation schemes are lacking in efficiency. In this paper, we propose an efficient revocable CP-ABE (R-CP-ABE) scheme for big data access control in cloud using proxy-based updates in which the proxy server performs the ciphertext and secret key updates instead of data owner and data user respectively during revocation. This outsourced updates during revocation reduces the communication and computation overhead of data owner and data users. In security analysis, we prove that our R-CP-ABE scheme is secure against chosen plain-text and user collusion attacks. In addition, we also show that our scheme achieves forward and backward secrecy. The performance analysis demonstrates that our method is efficient when comparing with existing schemes.

User Collusion Avoidance CP-ABE With Efficient Attribute Revocation for Cloud Storage

Attribute-based encryption (ABE) can guarantee confidentiality and achieve fine-grained data access control in a cloud storage system. Due to the fact that every attribute in ABE may be shared by multiple users and each user holds multiple attributes, any single-attribute revocation for some user may affect the other users with the same attribute in the system. Therefore, how to revoke attribute efficiently is an important and challenging problem in ABE schemes. In order to solve above problems, we first give a concrete attack to the existing ABE scheme with attribute revocation. Then, we formalize the definition and security model, which model collusion attack executed by the existing users cooperating with the revoked users. Finally, we present a user collusion avoidance ciphertext-policy ABE scheme with efficient attribute revocation for the cloud storage system. The problem of attribute revocation is solved efficiently by exploiting the concept of an attribute group. When an attribute is revoked from a user, the group manager updates other users’ secret keys. Furthermore, we prove that the proposed scheme is secure against collusion attack launched by the existing users and the revoked users. The security of the proposed scheme is reduced to the computational Diffie–Hellman assumption.

Improved collusion‐resisting secure nearest neighbor query over encrypted data in cloud

SummarySecurely performing nearest neighbor query over encrypted data in cloud is an important topic in the area of cloud computing, for which Wang et al recently put forward a scheme (ie, CloudBI‐II) to address the challenging security problem: resisting the collusion of cloud server and query users. In this paper, we propose an efficient attack method that indicates CloudBI‐II will reveal the difference vectors under the collusion attack. Furthermore, we show that the difference vector disclosure will result in serious privacy breach and, thus, attain an efficient attack method to break CloudBI‐II. Namely, CloudBI‐II cannot achieve their declared security. Through theoretical analysis and experiment evaluation, we confirm that our proposed attack approach can fast recover the original data from the encrypted data set in CloudBI‐II. Finally, we provide an enhanced scheme that can efficiently resist the collusion attack.

Improving Privacy-Preserving and Security for Decentralized Key-Policy Attributed-Based Encryption

Decentralized attribute-based encryption (ABE) is an efficient and flexible multi-authority attribute-based encryption system, since it does not requires the central authority and does not need to cooperate among the authorities for creating public parameters. Unfortunately, recent works show that the reality of the privacy preserving and security in almost well-known decentralized key policy ABE (KP-ABE) schemes are doubtful. How to construct a decentralized KP-ABE with the privacy-preserving and user collusion avoidance is still a challenging problem. Most recently, Y.Rahulamathavam et al. proposed a decentralized KP ABE scheme to try avoiding user collusion and preserving the user’s privacy. However, we exploit the vulnerability of their scheme in this paper at first and present a collusion attack on their decentralized KP-ABE scheme. The attack shows the user collusion cannot be avoided. Subsequently, a new privacy-preserving decentralized KP-ABE is proposed. The proposed scheme avoids the linear attacks at present and achieves the user collusion avoidance. We also show that the security of the proposed scheme is reduced to decisional bilinear Diffie–Hellman assumption. Finally, numerical experiments demonstrate the efficiency and validity of the proposed scheme.

Securing Outsourced Data in the Multi-Authority Cloud with Fine-Grained Access Control and Efficient Attribute Revocation

Data outsourcing is a promising service for data owners, where their data are stored on a cloud storage provider. Since the cloud is not fully trusted, data access control has become a challenging issue in the Cloud Storage System (CSS). Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a feasible technique for ensuring access control in the CSS, where an attribute authority is responsible to manage attributes and distribute keys. In this paper, we propose a novel revocable Multi-Authority CP-ABE scheme, in which the access policy can be constructed as an arbitrary tree rather than a matrix used by existing schemes. The tree-like policy makes our scheme more flexible. Consequently, the encryption, decryption and attribute revocation operations are also more efficient. Our scheme is also proved to be secure under the standard assumption. It can resist user collusion attack, while the attribute revocation operation also achieves both forward security and backward security. Simulation results show that our scheme is highly efficient

User Collusion Avoidance Scheme for Privacy-Preserving Decentralized Key-Policy Attribute-Based Encryption

Decentralized attribute-based encryption (ABE) is a variant of multi-authority based ABE whereby any attribute authority (AA) can independently join and leave the system without collaborating with the existing AAs. In this paper, we propose a user collusion avoidance scheme which preserves the user's privacy when they interact with multiple authorities to obtain decryption credentials. The proposed scheme mitigates the well-known user collusion security vulnerability found in previous schemes. We show that our scheme relies on the standard complexity assumption (decisional bilienar Deffie-Hellman assumption). This is contrast to previous schemes which relies on non-standard assumption (q-decisional Diffie-Hellman inversion).

Privacy-preserving distributed location proof generating system

The rapid development of location-based service (LBS) drives one special kind of LBS, in which the service provider verifies user location before providing services. In distributed location proof generating schemes, preventing users from colluding with each other to create fake location proofs and protecting user's location privacy at the same time, are the main technical challenges to bring this kind of LBS into practical. Existing solutions tackle these challenges with low collusion-detecting efficiency and defected collusion-detecting method. We proposed two novel location proof generating schemes, which inversely utilized a secure secret-sharing scheme and a pseudonym scheme to settle these shortcomings. Our proposed solution resists and detects user collusion attacks in a more efficient and correct way. Meanwhile, we achieve a higher level of location privacy than that of previous work. The correctness and efficiency of our proposed solution is testified by intensive security analysis, performance analysis, as well as experiments and simulation results.