Aggregation Scheme Research Articles

A privacy-preserving multidimensional data aggregation scheme with secure query processing for smart grid

A privacy-preserving multidimensional data aggregation scheme with secure query processing for smart grid

A fuzzy content-based group recommender system with dynamic selection of the aggregation functions

Recommender systems are currently software tools that are focused on providing users with the best choices in an overloaded search space of possible options. Hence, group recommender systems have recently become an important trend in recommendation, because they aim at recommending a special type of items so-called social items, that tend to be consumed in groups such as TV programs, travel packages, etc. Among the different types of algorithms applied for group recommender systems, this paper is focused on content-based group recommender systems, as a novel group recommendation paradigm that exploits item features in the recommendation generation process. Specifically, our goal is to introduce a new content-based group recommendation approach, based on the recommendation aggregation paradigm whose main novelty is the development of a dynamic selection process of the aggregation scheme. Such an approach is centered on the identification of group's characteristics that are matching with the most appropriate function to use in the individual recommendation aggregation step. To perform such a matching, it is proposed a fuzzy decision tree induction process. The experimental evaluation shows that this scheme improves the recommendation performance of previous content-based group recommendation approaches, as well as it serves a starting point for further research based on this dynamic selection paradigm.

Certificateless Aggregated Signcryption Scheme (CLASS) for Cloud-Fog Centric Industry 4.0

Over recent years, the Industrial Internet of Things and connectivity of the various sensors on the industrial and automaton front have played a crucial role in the manufacturing process. Production ventures are predominantly represented by Industry 4.0 so produce colossal information. Data outsourcing is one of the ways to manage the overhead of the massive data generated from the various resource-constrained devices utilized in the industrial environment. Therefore, the crowdsourced data from many organizations are outsourced to the cloud system. However, privacy and security challenges such as illegal admittance, data leakage are raised by the outsourced storage. Data authentication is an optimistic approach to establishing the integrity, confidentiality, and authenticity of the data. The certificateless signcryption scheme is most appropriate for lightweight devices established in the industrial ecosystem. In this article, we propose a privacy-conserving, lightweight data aggregation scheme to attain security in an industrial network. In the proposed model, the data owner collects the industrial data from various resource-constrained devices and sends this data to the data aggregator and proficiently data obtained by the industrial data user securely. Particularly, in this article, we propose a proficient certificateless aggregated signcryption scheme, which provides a data aggregation element in comparison to existing schemes. Our proposed scheme includes mutual authentication, public viability, integrity and confidentiality of data, volatile to key escrow, and privacy-preserving aspects for the industrial data. Performance evaluation and result analysis demonstrate that the proposed protocol performs better than other schemes significantly.

Investigation of the role of prolines 232/233 in RTPPK motif in tau protein aggregation: An in vitro study

Disease-related tau protein in Alzheimer's disease is hyperphosphorylated and aggregates into neurofibrillary tangles. The cis-proline isomer of the pSer/Thr-Pro sequence has been proposed to act as a precursor of aggregation (‘Cistauosis’ hypothesis), but this aggregation scheme is not yet entirely accepted. Hence to investigate isomer-specific-aggregation of tau, proline residues at the RTPPK motif were replaced by alanine residues (with permanent trans configuration) employing genetic engineering methods. RTPAK, RTAPK, and RTAAK mutant variants of tau were generated, and their in vitro aggregation propensity was investigated using multi-spectroscopic techniques. Besides, the cell toxicity of oligomers/fibrils was analyzed and compared to those of the wild-type (WT) tau. Analyses of mutant variants have shown to be in agreement (to some degree) to the theory of the ‘cistauosis’ hypothesis. The results showed that the trans isomer in the 232-rd residue (P232A mutant rather than P233A) had reduced aggregation propensity. However, this study did not illustrate any statistically significant difference between the wild and the mutant protein aggregations concerning cell toxicity.

DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning

Secure aggregation is widely used in horizontal federated learning (FL), to prevent the leakage of training data when model updates from data owners are aggregated. Secure aggregation protocols based on homomorphic encryption (HE) have been utilized in industrial cross-silo FL systems, one of the settings involved with privacy-sensitive organizations such as financial or medical, presenting more stringent requirements on privacy security. However, existing HE-based solutions have limitations in efficiency and security guarantees against colluding adversaries without a Trust Third Party. This paper proposes an efficient Doubly Homomorphic Secure Aggregation (DHSA) scheme for cross-silo FL, which utilizes multi-key homomorphic encryption (MKHE) and seed homomorphic pseudorandom generator (SHPRG) as cryptographic primitives. The application of MKHE provides strong security guarantees against up to \(N-2\) participates colluding with the aggregator, with no TTP required. To mitigate the large computation and communication cost of MKHE, we leverage the homomorphic property of SHPRG to replace the majority of MKHE computation by computationally friendly mask generation from SHPRG, while preserving the security. Overall, the resulting scheme satisfies the stringent security requirements of typical cross-silo FL scenarios, at the same time providing high computation and communication efficiency for practical usage. We experimentally demonstrate that our scheme brings a speedup to 20\(\times \) over the state-of-the-art HE-based secure aggregation and reduces the traffic volume to approximately 1.5\(\times \) inflation over the plain learning setting.

Lattice‐based batch authentication scheme with dynamic identity revocation in VANET

Aggregate signatures allow someone to aggregate multiple signatures into one signature, which is suitable for resource-constrained and computationally inefficient environments. Identify-based aggregate signature can solve the storage problem of public key certificates while achieving efficient signature verification. However, in most of the identity-based aggregate signature schemes, the user identity revocation process is time-consuming and cannot resist quantum attacks. To solve above problems, this paper proposes a lattice-based aggregate signature scheme with dynamic identity revocation by combining lattice-based cryptography and an aggregate signature scheme. The security of the proposed lattice-based aggregate signature scheme with dynamic identity revocation has been proved in the random oracle model. In addition, the verification efficiency of the aggregate signature has been improved compared with multiple different signatures. Much of the data transfer in Vehicular Ad Hoc Network (VANET) is carried out wirelessly, which makes VANET vulnerable to identity spoofing attacks. Identity authentication technology can prevent attackers from impersonating legitimate users, thus ensuring the security of VANET. Based on the proposed lattice-based aggregate signature scheme with dynamic identity revocation, this paper proposes a lattice-based batch authentication scheme with dynamic identity revocation in VANET. Through the proposed batch authentication scheme, we can effectively resist the impersonation attack of VANET in the quantum computer environment, and the efficiency of authentication is improved.

Inference of Wi-Fi busy time fraction based on Markov chains

IEEE 802.11 has emerged as a vital wireless network access technology for mobile devices. By providing the potential for high connectivity speeds, this technology has led to a huge rise in the number of access points (APs). In such environments, mobile devices have the choice to join several Wi-Fi networks. Despite its importance to user Quality of Experience (QoE), the AP selection is still trivial since it focuses at best on the received signal strength if not only the user’s history. Crucial metrics that capture the overall dynamics of the AP load condition, such as the network load, are not taken into account. In this paper, we propose to use the Busy Time Fraction (BTF) as a metric to choose the best AP to attach to. The BTF level of a given channel is inferred based on the frame aggregation scheme proposed since the 802.11n standard. In this regard, we build a proof of concept system, FAM (Frame Aggregation based method) that leverages the theoretical frame aggregation levels of a probe traffic returned by two analytical Markovian models and the measured ones in order to estimate not only the BTF but also the nature of the traffic. We validate the accuracy of our proposed approach against ns-3 simulations under several scenarios.

Implications of aggregation uncertainty in Data Envelopment Analysis: An application in incentive regulation

Researchers and practitioners who use Data Envelopment Analysis often want to incorporate several inputs and outputs in their model to consider as much relevant information as possible. However, too many inputs and outputs can result in the well-known dimensionality problem referred to as the “curse of dimensionality”. Several studies suggest how to solve, or at least reduce, this problem. One solution is to aggregate the inputs and outputs before using them in the model. This paper examines the implications when the methods used to aggregate the inputs and outputs contain uncertainty. The uncertainty can, for example, be price uncertainty if we use input and/or output prices for the aggregation. We show that the implications for a given unit depend entirely on its input and output mixes relative to those of its peers, and that the implications are higher the more heterogeneous the sector is. As an example, we use the Danish benchmarking regulation of the waste water companies. We find that uncertainty in the regulator’s aggregation scheme does not, on average, influence the companies’ efficiency scores a lot. Still, individual companies can be greatly affected by this uncertainty.

ES-PPDA: an efficient and secure privacy-protected data aggregation scheme in the IoT with an edge-based XaaS architecture

In an Internet of Things (IoT) system based on an anything as a service (XaaS) architecture, data are uploaded from heterogeneous nodes in a nonstandardized format and aggregated on the server side. Within this data-intensive architecture, privacy preservation is one of the most important issues. In response to this concern, there are numerous privacy-protection data aggregation (PPDA) solutions available for various IoT applications. Because of the limited resources of intelligent IoT devices, traditional PPDA cannot meet practical privacy and performance needs. To tackle this challenge, we provide a more efficient and secure PPDA solution that guarantees data security and integrity through Paillier homomorphic encryption and online/offline signing technology. Detailed security analysis shows that our system is unpredictable under a chosen message attack, and the data integrity may be guaranteed under the assumption of q-strong Diffie-Hellman (Q-SDH). We choose an M/G/1 priority queue model to maximize system performance. M/G/1 enhances queuing efficiency and accelerates channel access, thus reducing waiting time and increasing reliability. The experimental results show that our data aggregation scheme is reliable with low latency.

Provably Secure Linearly Homomorphic Aggregate Signature Scheme for Electronic Healthcare System

In recent years, deploying Internet of Things (IoT) in electronic healthcare systems (EHS) has made great progress in healthcare detection. It is extremely important to reduce the cost of communication and ensure the authenticity and integrity of data. A linearly homomorphic signature scheme can solve the above problems. However, when the scale of EHS is too large, the transmission, storage and verification of signatures need a high cost. An aggregate signature can combine many signatures generated by many different users into a short one. Therefore, only one aggregate signature needs to be processed during verification, transmission and storage. Combining the advantages of aggregate signature and linearly homomorphic signature, this paper proposes an aggregate signature scheme based on a linearly homomorphic signature for EHS, which has both linear homomorphism and aggregation, and realizes double data compression. Moreover, our scheme can resist a potential real attack, named a coalition attack. The security of this scheme is rigorously demonstrated based on the computational Diffie–Hellman assumption in the random oracle model.

Node localization and data aggregation scheme using cuckoo search and neural network

AbstractAmong multi‐hop technology, wireless sensor network (WSN) has been extensively investigated owing to its potential application in vivid fields. However, a key issue probing WSN is node location that is also the major area of interest in the present paper. The paper takes advantage of cuckoo search (CS) as the swarm intelligence technique used to address the issues of identification of malicious or unknown nodes within the network. The distance vector (DV)‐hop is used to determine the distance between the anchor sensor node and the unknown or the node with compromised nature. Then, artificial neural network architecture is used to distinguish the nodes based on the characteristics. This is followed by the evaluation of the proposed scheme to offer reliable data transmission using CS optimized data aggregation scheme. The simulation analysis over 1000 deployed nodes shows that CS significantly decreases the localization error to 0.494 and localization time to 0.058 s along with 15%–20% improvement in the throughput and packet delivery ratio. This shows that the proposed CS optimized architecture is successful in identifying the position of unknown nodes as well as compromised nodes that significantly improved the reliability of the data transmission.

Secure and efficient multifunctional data aggregation without trusted authority in edge-enhanced IoT

Since the rapid development of Internet of Things (IoT) has promoted the dramatic growth of data, data aggregation has received considerable attention, which can collect the sensed data from IoT devices and analyze them for high-quality information to greatly benefit our life. Nevertheless, these sensed data may be related to individual sensitive information and are vulnerable to security threats. To solve the problem, many secure data aggregation schemes have been proposed. However, most of them either have a high overhead, only support sum aggregation, or need the involvement of a trusted third party. They cannot strike a good balance between security, efficiency and functionality. In this paper, we propose a Secure and Efficient Multifunctional Data Aggregation without Trusted Authority (TA) in Edge-Enhanced IoT (SEMDA). In our method, combined with lightweight cryptographic techniques, a robust, multifunctional and fine-grained data aggregation scheme is constructed. In addition, considering the potential privacy disclosure caused by deep association between IoT data, we extend SEMDA to support differential privacy. A comprehensive security analysis of SEMDA is conducted under the honest-but-curious model, and the results show that our scheme not only achieves confidentiality and privacy, but also guarantees integrity and authentication. Meanwhile, extensive theoretical analyses and experimental evaluations demonstrate that SEMDA performs more efficiently with respect to computation and communication while retaining more desired properties.

Privacy-Preserving and Fault-Tolerant Aggregation of Time-Series Data With a Semi-Trusted Authority

Time-series data aggregation in Internet of Things applications is a useful operation, where the time-series data is sensed by a group of users, and gathered by the aggregator for real-time analysis. However, some security and privacy challenges still affect the collection and aggregation process. Although existing privacy-preserving solutions achieve strong privacy guarantees, they introduce a fully trusted TA that is difficult to realize in the real world. Besides, they cannot be directly applied in time-series data aggregation scenarios due to unacceptable efficiency. In this article, we propose a privacy-preserving time-series data aggregation scheme with a semi-trusted authority. Moreover, our scheme also supports arbitrary aggregate functions and fault tolerance to enhance the reliability and scalability of data aggregation. Security analysis demonstrates that our proposed scheme achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n-k)$ </tex-math></inline-formula> -source anonymity even if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k(k\leq (n-2))$ </tex-math></inline-formula> data providers collude with the cloud server. We also conduct thorough experiments based on a simulated data aggregation scenario to show the high computation and communication efficiency of our scheme.

An improved pairing-free certificateless aggregate signature scheme for healthcare wireless medical sensor networks.

In healthcare wireless medical sensor networks (HWMSNs), the medical sensor nodes are employed to collect medical data which is transmitted to doctors for diagnosis and treatment. In HWMSNs, medical data is vulnerable to various attacks through public channels. In addition, leakage of patients’ information happens frequently. Hence, secure communication and privacy preservation are major concerns in HWMSNs. To solve the above issues, Zhan et al. put forward a pairing-free certificateless aggregate signature (PF-CLAS) scheme. However, according to our cryptanalysis, the malicious medical sensor node (MSNi) can generate the forged signature by replacing the public key in the PF-CLAS scheme. Hence, to address this security flaw, we design the improved PF-CLAS scheme that can achieve unforgeability, anonymity, and traceability. Since we have changed the construction of the partial private key, the improved PF-CLAS scheme can resist Type I and Type II attacks under the Elliptic Curve Discrete Logarithm assumption. In terms of the performance evaluation, the proposed scheme outperforms related CLAS schemes, which is more suitable for HWMSNs environments.

IV-GNN : interval valued data handling using graph neural network.

Interval-valued data is an effective way to represent complex information where uncertainty, inaccuracy etc. are involved in the data space and they are worthy of taking into account. Interval analysis together with neural network has proven to work well on Euclidean data. However, in real-life scenarios, data follows a much more complex structure and is often represented as graphs, which is non-Euclidean in nature. Graph Neural Network is a powerful tool to handle graph like data with countable feature space. So, there is a research gap between the interval-valued data handling approaches and existing GNN model. No model in GNN literature can handle a graph with interval-valued features and, on the other hand, Multi Layer Perceptron (MLP) based on interval mathematics can not process the same due to non-Euclidean structure behind the graph. This article proposes an Interval-Valued Graph Neural Network, a novel GNN model where, for the first time, we relax the restriction of the feature space being countable without compromising the time complexity of the best performing GNN model in the literature. Our model is much more general than existing models as any countable set is always a subset of the universal set , which is uncountable. Here, to deal with interval-valued feature vectors, we propose a new aggregation scheme of intervals and show its expressive power to capture different interval structures. We validate our theoretical findings about our model for graph classification task by comparing its performance with those of the state-of-the-art models on several benchmark and synthetic network datasets.

Подход к оценке динамики изменения энергопотребления в регионе на основе использования нечеткой логики

Currently, energy efficiency indicators of regional infrastructure are presented as a rule in a quantitative form. However, decision makers need an assessment in a readable form to see the transition from a few quantitative values to a qualitative one that allows us in general to assess the dynamic patterns of energy consumption in the region. In this regard, the development of a method to assess the efficiency of energy consumption with linguistic recognition of the results obtained seems to be very relevant. The analysis is based on the retrospective values of indicators of the fuel and energy balances of the regions. The consolidated balance sheet includes about 500 indicator values for all used fuel and energy resources per year. To assess the multifactorial system under consideration, it is advisable to use the well-known matrix aggregation scheme, where the rows are the factors, and the columns are their qualitative levels. A standard five-level fuzzy 01-classifier is used as a scale for the transition from quantitative values of balance sheet indicators to qualitative values. A method is proposed to assess the dynamic pattern of energy consumption by means of linguistic recognition of a certain set of relative indicators of fuel and energy balances. Evaluation of the results in several regions using actual retrospective data has showed the relevance and reliability of the results obtained. At the same time, the results have been checked by methods of analysis and visualization of multidimensional data, as well as by experts. It has been established that the proposed approach to assess the efficiency of the use of fuel and energy resources can be used in the region policy of energy saving to improve the efficiency of decision-making.

A Traceable and Anonymous Data Aggregation Scheme with Fog Computing Devices for Smart Grid

As an extension of the Internet of Things (IoT), smart city is a new aim for applications used in different industrial aspects. Intelligent IoT devices are used everywhere in smart cities to implement the functions such as monitoring and managing. Smart grid is one of the critical parts. Obtaining the quantity and cost of electricity usage is the most critical task of the smart grid. But such data in plaintext may leak the user privacy. So, it is an emergent aim to protect the private information of the user. Thus, we present a secure scheme for the smart grid, which not only protects the user’s information including identity and power consumption in the communications but also tracks the correct electricity cost for each user. Also, electricity consumption data from users could be aggregated in fog devices and then analyzed by the utility provider. The formal proof shows that the message transmission reaches the security level of the chosen plaintext attack (CPA). Also, the security properties of the scheme express the robustness. Finally, the performance study demonstrates that the proposed protocol is acceptable in practice.

Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT

With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.

Reinforcement Learning-Based Near-Optimal Load Balancing for Heterogeneous LiFi WiFi Network

Owing to the nonoverlapping spectrum, light fidelity (LiFi) and WiFi technologies can coexist and form a heterogeneous LiFi WiFi network (HLWN). The performance of HLWN significantly depends upon the load balancing strategies. Since load balancing of HLWN is a nonconvex mixed-integer nonlinear programming optimization problem, it is mathematically intractable, and therefore, the conventional optimization methods fail to provide an optimal global solution. Although an optimal solution can be obtained using the exhaustive search method, it would be computationally complex. Therefore, in this article, a reinforcement learning (RL)-based algorithm is explored for solving the load balancing problem for the downlink HLWN at reasonably low complexity and near optimal performance. We have proposed three different reward functions for RL; the first and second reward functions work toward maximizing average network throughput and user satisfaction, respectively. The third reward function is designed to maximize the long-term system throughput and ensure at least 50% user’s satisfaction for all users. In order to study the effects of link aggregation on the system performance, this article considers two different types of receiver schemes, namely, single access point (SAP) and link aggregation (LA) scheme. While the SAP allows the user to receive data only from an SAP, the LA scheme allows the user to receive data simultaneously from both LiFi and WiFi AP. This article also includes effect of random orientation of the receiver device and handover overhead. Furthermore, concepts of domain knowledge have been included in this article to reduce the computational complexity of the algorithm. The proposed system performance is compared with the the following two benchmarks: received signal strength (RSS) and exhaustive search based on the computational complexity, average system throughput, and user satisfaction. It is shown that the proposed RL scheme outperforms the RSS scheme in average system throughput and user satisfaction. The RL scheme with an appropriate reward function provides a matching performance to the exhaustive search at reasonably low complexity.

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.