Dynamic User Grouping Research Articles

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.

An Adaptively Secure and Efficient Data Sharing System for Dynamic User Groups in Cloud

An Adaptively Secure and Efficient Data Sharing System for Dynamic User Groups in Cloud

Untouchable Once Revoking: A Practical and Secure Dynamic EHR Sharing System via Cloud

Healthcare Internet-of-Things (IoT) enables lightweight devices to observe patients’ vital signals and outsource them to a remote cloud to enjoy flexible data sharing. However, it faces many security threats as the outsourced data is no longer physically controlled by data owners, and the cloud that hosts the outsourced data is not fully trusted. Many privacy protection technologies have been adopted to solve this problem, among which cryptographic mechanisms have become one of the most promising tools. Unfortunately, current cryptographic mechanisms in healthcare IoT mainly suffer from the following challenges: 1) dynamic user groups for managing users’ accessibility; 2) efficient revocation mechanism to mitigate the burden during user revocation; 3) forward and backward secrecy to ensure session independence in the presence of session key leakage; 4) revocable storage to prevent data users from learning any unauthorized data even the data is authorized before; and 5) information manipulation during data transmission. In this article, we introduce a practical and secure system to address the above problems. Our system provides fine-grained access control with dynamic user groups for optimizing scalability and functionality. We prove that our system is secure against numerous real-world threats. Extensive comparison and experimental analysis demonstrate that our system enjoys superior performance than the state-of-the-art solutions.

An efficient and privacy-preserving data aggregation scheme supporting arbitrary statistical functions in IoT

The Internet of Things (IoT) has profoundly impacted our lives and has greatly revolutionized our lifestyle. The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions. However, the high frequency of collecting user data will raise people concerns about personal privacy. In recent years, many privacy-preserving data aggregation schemes have been proposed. Unfortunately, most existing schemes cannot support either arbitrary aggregation functions, or dynamic user group management, or fault tolerance. In this paper, we propose an efficient and privacy-preserving data aggregation scheme. In the scheme, we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence. On this basis, the proposed scheme supports not only arbitrary aggregation functions, but also flexible dynamic user management. Furthermore, the scheme achieves fault-tolerant capabilities by utilizing a future data buffering mechanism. Security analysis reveals that the scheme can achieve the desired security properties, and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

A Lightweight Auditing Service for Shared Data with Secure User Revocation in Cloud Storage

As data sharing has become one of the most popular services offered by cloud storage, designing public auditing mechanisms for integrity of shared data becomes more important. Two problems which arise in shared data auditing include preserving users identity and collusion resistant revocation of users. When data stored at the cloud is shared among a group of users, different users may modify and sign different data blocks which leaks signer identities to the public verifier. Also, when a user is revoked from the group, signatures generated by this user should be re-signed by the cloud server using re-signature keys. In addition, collusion of cloud server and the revoked user should leak no information about the private key of other users. In this paper, by employing a new proxy re-signature scheme, we propose a public shared data auditing mechanism that provides identity privacy and collusion resistant user revocation, simultaneously. The proposed protocol requires only lightweight computations at the user side for signing data blocks in real-time online phase. Moreover, our protocol supports large dynamic group of users, batch auditing and dynamic data operations. Experimental results demonstrate excellent efficiency of our scheme in comparison to the state of the art.

Server-Aided Bilateral Access Control for Secure Data Sharing With Dynamic User Groups

As a versatile technique, cloud-fog computing extends the traditional cloud server to offer various on-demand data services. Maintaining data confidentiality is one of the most crucial requirements for data services, many cryptosystems have been proposed to reserve information privacy against such an untrusted environment. However, in cloud-fog computing, how to confidentially and efficiently share data and fetch desirable data without expensive data decryption for resource-constrained end-devices is challenging. In this paper, we propose a cloud-fog system for the Internet-of-Things (IoT) ecosystem by introducing a cryptographic primitive called server-aided revocable bilateral attribute-based encryption (SRB-ABE). Our solution is a secure and lightweight bilateral access control system with dynamic user groups, including (1) fine-grained data user and data owner access control simultaneously; (2) outsourced data source identification; (3) server-aided user revocation with publicly updatable ciphertexts; and (4) lightweight data decryption mechanism with one exponentiation computation. We present the formal definition and concrete construction of SRB-ABE with security proofs to build cloud-fog systems. The extensive comparison and experimental analysis demonstrate that our construction has superior functionality and comparable performance than the most relevant solutions.

Performance analysis of Power-Domain NOMA and NOMA-2000 on AWGN and Rayleigh fading channels

Non-Orthogonal Multiple Access (NOMA) has been a hot research topic over the past years, because it is widely viewed as a promising technology for 5G cellular networks and beyond. The NOMA literature today is heavily based on the so-called Power-Domain NOMA (PD-NOMA), which consists of superposing user signals with different powers. Also, the focus in the literature has been on the derivation of achievable rates and other information theoretic measures. In contrast, the performance degradation that is caused by multi-user interference at practical bit error rate (BER) values has not attracted much attention thus far. Taking a major deviation from the current trend, some of the present authors recently revived an early NOMA concept, which had been rather overlooked in the literature. This concept, which we refer to as NOMA-2000 due to its year of introduction, consists of superposing the signals of two user groups instead of superposing the signals of two single users. In subsequent papers, performance of NOMA-2000 has been investigated in different scenarios, but no comparisons with PD-NOMA have been provided. The purpose of this paper is to fill this gap and compare the BER performances of the two schemes using different channel models, channel overload factors, and power imbalance levels between users. For the Rayleigh fading channel, we consider both static user grouping and dynamic user grouping. Our results give a clear insight into the practical performance limitations of PD-NOMA and the improvements that can be achieved with NOMA-2000 in most cases.

DM-GKM: A key management scheme for dynamic group based applications

In recent years, group-based applications (GBAs) have gained popularity due to their interesting and promising functionalities such as video on demand, teleconferencing, and pay per view. The advancements in wireless networks and the emergence of mobile devices such as smartphones and tablets have also increased the demands for GBAs.However, the implementation of group key management protocols for GBAs leads to significant computational, storage and communication overheads as well as potential system bottlenecks due to the high mobility of group members. The goal of this research is to address these issues and design a lightweight key management framework that requires fewer computations of keys for dynamic mobile users. A new group key management framework is proposed in this research, which is called the “DynaMic Group Key Management” (DM-GKM) framework. This framework exploits the advantages of the asymmetric key cryptosystem in order to guarantee security and it alleviates the rekeying overhead and distributing the independent Group Key (GK) for each cluster. Simulation and performance analysis demonstrates that the DM-GKM framework fulfils the requirements of a lightweight key management framework for large, dynamic groups of users. An analytical model is also developed to determine the performance and security features of the proposed framework.

A D2D-Based Solution for MTC Connectivity Problem in NOMA-Based Cellular IoT Networks: Dynamic User Grouping and Resource Allocation

In a cellular network (CN), cellular users (CUs) located nearby machine type communications (MTC) devices (MTC-Ds) may act as uplink gateways to relay data to the base station (BS). We consider non-orthogonal multiple access (NOMA) and successive interference cancellation (SIC) at the receiver to increase the number of connected devices and the spectrum efficiency. Both underlay and overlay spectrum access modes (SAMs) are considered. We introduce a dynamic user grouping (UG) concept as a practical constraint for SIC, in order to decrease the receiver complexity. Moreover, SIC constraints are introduced based on a minimum signal-to-noise-plus-interference ratio (SINR) at the receiver. We formulate a joint dynamic UG, power allocation, and resource block (RB) assignment problem, aiming at maximizing the total sum-rate of both CUs and MTC-Ds. We transform the obtained non-convex mixed-integer programming problem into a convex problem by using a quadratic fractional programming (FP). A heuristic method is also proposed to reduce complexity. Simulation results demonstrate that the proposed solution outperforms the conventional CN (C-CN) method and the results of the proposed optimization methods are close to optimal solution obtained by the exhaustive search (ES) algorithm in terms of total average sum-rate and network connectivity, while requiring less transmit power. The heuristic method decrease the computational complexity of the FP method at the expense of a small reduction in spectrum efficiency.

TelMED: Dynamic User Clustering Resource Allocation Technique for MooM Datasets Under Optimizing Telemedicine Network

Tele-Medical data communication via general purpose networking protocols and techniques are major set-back under low line channels and adequate resources such as bandwidth, frequency, power spectrum for transmission and impacts the Quality of Data (QoD) on transmission line. In this paper, a heterogeneous multi-input multi-out (MIMO) based dynamic user clustering technique is proposed and the protocol is termed as TelMED. The proposed technique introduces machine learning terminology on networking nodes for dynamic user grouping and classification resulting in the formation of clusters with reflective similarity indexing ratio. The dynamic clustered users of TelMED protocol are allocated with resources for the transmission of Multi-Objective Optimized Medical datasets resulting in creation of virtual telemedicine networking environment with a given typical network space. The technique is designed on clustered user grouping size of maximum 32 users for reliable results over a fixed networking space and optimized resources for low line transmission channels of rural or remote networks. The resulting technique proves an efficiency of 92.3% over dynamic MIMO user grouping.

Secure Dynamic Groups Data Sharing with Modified Revocable Attribute-Based Encryption in Cloud

Cloud computing provides a flexible and convenient way for data sharing, which brings various benefits for both the society and individuals. But there exists a natural resistance for users to directly outsource the shared data to the cloud server since the data often contain valuable information. Although it causes many security issues, cloud service providers are not at the same level of trust as users. To preserve the privacy of data against non-trusted Cloud Service Provider (CSP) files, current solutions implement Cryptographic methods (for example, encryption methods) and deliver decryption keys only to authorized users. However, data sharing in the cloud among authorized users remains a difficult problem, especially when it comes to dynamic user groups. Most of the research on dynamic group data exchange has been done in the cloud with many algorithms, such as Attribute-Based Encryption (ABE), Ciphertext Attribute-Based Encryption (CP-ABE) to provide better security in dynamic cloud users with multiple authorities, but they still face challenges, either lack of performance or rely on a trusted server, and are not suitable for distribution with the problem of eliminating attributes. Thus, the Revocation user cannot get shared data before and after. To solve this in particular, we first suggest an effective Modified Revocable Attribute-Based Encryption (MR-ABE) system with the quality of ciphertext allocation by applying and integrating both Identity-Based Encryption (IBE) and CP-ABE techniques. It can provide confidential forward / backward of encrypted data by delivering user revocation attributes and updating encrypted text simultaneously. Next, we perform Fine-grained access control and data exchange for on-demand services with dynamic user groups on the cloud. Experimental data show that our proposed system is more efficient and scalable than the latest generation solutions.

Integrity Audit of Shared Cloud Data with Identity Tracking

More and more users are uploading their data to the cloud without storing any copies locally. Under the premise that cloud users cannot fully trust cloud service providers, how to ensure the integrity of users’ shared data in the cloud storage environment is one of the current research hotspots. In this paper, we propose a secure and effective data sharing scheme for dynamic user groups. (1) In order to realize the user identity tracking and the addition and deletion of dynamic group users, we add a new role called Rights Distribution Center (RDC) in our scheme. (2) To protect the privacy of user identity, when performing third party audit to verify data integrity, it is not possible to determine which user is a specific user. Therefore, the fairness of the audit can be promoted. (3) Define a new integrity audit model for shared cloud data. In this scheme, the user sends the encrypted data to the cloud and the data tag to the Rights Distribution Center (RDC) by using data blindness technology. Finally, we prove the security of the scheme through provable security theory. In addition, the experimental data shows that our proposed scheme is more efficient and scalable than the state-of-the-art solution.

Secure and flexible economic data sharing protocol based on ID-based dynamic exclusive broadcast encryption in economic system

Abstract Sharing economic data is paramount for improving quality and developing more efficient ways to produce statistics, and making better economic decisions. The economic data is of great importance to the corporations and governments, and they must be protected against the outsiders. Unfortunately, in an economic administration system, a few users may be malicious, or they are at high risk to leak information to the outsiders. Therefore, the economic data must also be protected against these users. The traditional broadcast encryption can provide protected data sharing among honest users. However, it is not efficient when most of the users are honest, and only a small amount of users are malicious. The traditional method is not cost effective, and does not fit to the situation where the set of malicious users dynamically changes either. Meanwhile, in traditional broadcast encryption, the authorized users’ identities need to be sent with the ciphertext. The valid users’ anonymity is not provided. To solve these problems, in this work, we present a novel cryptographic primitive, i.e. ID-based Dynamic Exclusive Broadcast Encryption (IBDEBE), and based on a hybrid framework (the combination of the exponent-inversion framework and the commutative-blinding framework) we propose an IBDEBE scheme with constant-size private keys and ciphertexts. The IBDEBE scheme is proved to be semi-adaptively semantically secure in the random oracle model. By applying the IBDEBE scheme, a secure economic data sharing protocol is devised, which is efficient and flexible in dynamic honest user groups, and it provides good security properties, i.e. source authenticity, data integrity protection, data access control, resistance to collusion attack and anonymity. We evaluate the performance of our solution with experiments and the results show good computation efficiency.

Joint Clustering of Users and Resources for Multi-Cell VMIMO-SC-FDMA Uplink Systems

In this paper, we investigate the joint clustering of users and resources based on minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) equalization for multi-cell single carrier-frequency division multiple access (SC-FDMA) uplink systems. To exploit the properties of the MMSE-OSIC equalization, we formulate the dynamic user grouping and resource allocation as a clustering problem, where resources are divided into several clusters and users are assigned into different clusters. Furthermore, we develop a new efficient K-means type algorithm named iterative K-groups clustering algorithm (IKG_OSIC) to solve the optimization problem. By making use of the MMSE-OSIC results, IKG_OSIC algorithm largely reduces the computation complexity of the joint resource allocation problem. The simulation results show that the proposed algorithm achieves good tradeoff between the performance and complexity and has better system throughput than the existing algorithms for virtual multiple-input multiple-output single carrier-frequency division multiple access (VMIMO-SC-FDMA) uplink systems.

Intelligent MU-MIMO User Selection With Dynamic Link Adaptation in IEEE 802.11ax

IEEE 802.11ax high-throughput wireless access networks support multi-user multiple-input multiple-output (MU-MIMO)-based communication, where a set of spatially apart wireless stations forms a user group and uses different spatial streams for simultaneous transmission and reception. In this architecture, dynamic user group selection is an important aspect for maintaining high-throughput fair channel access. In addition, the physical and media access control parameters, like channel bonding levels, modulation, and coding schemes need to be tuned based on the selected user group to utilize the maximum available capacity. In this paper, we design an online learning-based approach over a centralized logical control architecture, called intelligent MU-MIMO user selection with link adaptation (IMMULA), where a central controller collects the performance statistics under various configuration space and applies a reinforcement learning strategy to select the best-suited configurations dynamically at periodic intervals. The performance of IMMULA is analyzed over a testbed consisting of 6 IEEE 802.11ac access points and 20 wireless stations. The results show that IMMULA improves network performances significantly compared to other baseline mechanisms.

Group layer MU-MIMO for 5G wireless systems

Multiple-input multiple-output (MIMO) and nonorthogonal multiple access technologies are considered as fundamental components to meet the high spectral efficiency requirements of the forthcoming 5G wireless systems and beyond. In this context, group layer multiuser MIMO (GL-MU-MIMO) scheme has been proposed by the authors with linear group multiuser detection and receive antenna selection (RAS) to increase the number of simultaneously and reliably connected users more than the utilized number of radio frequency chains at the base station. In this paper, we derive the sum rate and capacity region expressions for GL-MU-MIMO uplink Rayleigh fading channels to demonstrate the impact of power allocation on the system performance and user-fairness. In addition, two low complexity RAS algorithms are proposed to maximize the sum rate of designed users’ groups and overall channel capacity. These techniques are utilized for new dynamic user grouping, RAS, and power allocation strategy to optimize the system performance under total received power and minimum user rate constrains. Compared with the generic MU-MIMO, numerical simulations demonstrate valuable tradeoffs between user overloading, complexity, and achieved performance through efficient utilization of groups’ power allocation. The new outcomes of GL-MU-MIMO extends the state-of-the-art towards diverse multi-antenna applications for future communications.

Dynamic User Grouping-Based NOMA Over Rayleigh Fading Channels

With the development of wireless communications and internet of things (IoT), non-orthogonal multiple access (NOMA) has been considered as one of effective schemes to meet the rapidly growing user access requirements. Two types of NOMA, i.e., NOMA-2000 and power-domain NOMA (PD-NOMA), have been proposed in recent years. The first one is a superposition of a set of orthogonal frequency division multiple access (OFDMA) signals and a set of spread-spectrum multicarrier-code division multiple access (MC-CDMA) signals while the second one is a direct superposition of a MC-CDMA signal and an OFDMA signal. This paper proposes a dynamic user grouping method for classifying users and then compares the performance between NOMA-2000 and PD-NOMA over Rayleigh fading channels. Simulation results show that the PD-NOMA can always exhibit lower bit error rate (BER) than the NOMA-2000 under different signal-to-noise ratios. Under the Rayleigh fading environment, the performance of the PD-NOMA after user grouping is better than that of NOMA-2000.

Secure Fine-Grained Access Control and Data Sharing for Dynamic Groups in the Cloud

Cloud computing is an emerging computing paradigm that enables users to store their data in a cloud server to enjoy scalable and on-demand services. Nevertheless, it also brings many security issues, since cloud service providers (CSPs) are not in the same trusted domain as users. To protect data privacy against untrusted CSPs, existing solutions apply cryptographic methods (e.g., encryption mechanisms) and provide decryption keys only to authorized users. However, sharing cloud data among authorized users at a fine-grained level is still a challenging issue, especially when dealing with dynamic user groups. In this paper, we propose a secure and efficient fine-grained access control and data sharing scheme for dynamic user groups by: 1) defining and enforcing access policies based on the attributes of the data; 2) permitting the key generation center to efficiently update user credentials for dynamic user groups; and 3) allowing some expensive computation tasks to be performed by untrusted CSPs without requiring any delegation key. Specifically, we first design an efficient revocable attribute-based encryption (ABE) scheme with the property of ciphertext delegation by exploiting and uniquely combining techniques of identity-based encryption, ABE, subset-cover framework, and ciphertext encoding mechanism. We then present a fine-grained access control and data sharing system for on-demand services with dynamic user groups in the cloud. The experimental data show that our proposed scheme is more efficient and scalable than the state-of-the-art solution.

PDA: Semantically Secure Time-Series Data Analytics with Dynamic User Groups

Third-party analysis on private records is becoming increasingly important due to the widespread data collection for various analysis purposes. However, the data in its original form often contains sensitive information about individuals, and its publication will severely breach their privacy. In this paper, we present a novel Privacy-preserving Data Analytics framework PDA, which allows a third-party aggregator to obliviously conduct many different types of polynomial-based analysis on private data records provided by a dynamic sub-group of users. Notably, every user needs to keep only O(n) keys to join data analysis among O(2^n) different groups of users, and any data analysis that is represented by polynomials is supported by our framework. Besides, a real implementation shows the performance of our framework is comparable to the peer works who present ad-hoc solutions for specific data analysis applications. Despite such nice properties of PDA, it is provably secure against a very powerful attacker (chosen-plaintext attack) even in the Dolev-Yao network model where all communication channels are insecure.

PHOABE: Securely outsourcing multi-authority attribute based encryption with policy hidden for cloud assisted IoT

Attribute based encryption (ABE) is an encrypted access control mechanism that ensures efficient data sharing among dynamic group of users. Nevertheless, this encryption technique presents two main drawbacks, namely high decryption cost and publicly shared access policies, thus leading to possible users’ privacy leakage.In this paper, we introduce PHOABE, a Policy-Hidden Outsourced ABE scheme. Our construction presents several advantages. First, it is a multi-attribute authority ABE scheme. Second, the expensive computations for the ABE decryption process is partially delegated to a Semi Trusted Cloud Server. Third, users’ privacy is protected thanks to a hidden access policy. Fourth, PHOABE is proven to be selectively secure, verifiable and policy privacy preserving under the random oracle model. Five, estimation of the processing overhead proves its feasibility in IoT constrained environments.