Aggregation Protocol Research Articles

Secure and efficient parameters aggregation protocol for federated incremental learning and its applications

Federated Learning (FL) enables the deployment of distributed machine learning models over the cloud and Edge Devices (EDs) while preserving the privacy of sensitive local data, such as electronic health records. However, despite FL advantages regarding security and flexibility, current constructions still suffer from some limitations. Namely, heavy computation overhead on limited resources EDs, communication overhead in uploading converged local models' parameters to a centralized server for parameters aggregation, and lack of guaranteeing the acquired knowledge preservation in the face of incremental learning over new local data sets. This paper introduces a secure and resource-friendly protocol for parameters aggregation in federated incremental learning and its applications. In this study, the central server relies on a new method for parameters aggregation called orthogonal gradient aggregation. Such a method assumes constant changes of each local data set and allows updating parameters in the orthogonal direction of previous parameters spaces. As a result, our new construction is robust against catastrophic forgetting, maintains the federated neural network accuracy, and is efficient in computation and communication overhead. Moreover, extensive experiments analysis over several significant data sets for incremental learning demonstrates our new protocol's efficiency, efficacy, and flexibility.

Interplay between epigallocatechin-3-gallate and ionic strength during amyloid aggregation.

The formation and accumulation of protein amyloid aggregates is linked with multiple amyloidoses, including neurodegenerative Alzheimer’s or Parkinson’s disease. The mechanism of such fibril formation is impacted by various environmental conditions, which greatly complicates the search for potential anti-amyloid compounds. One of these factors is solution ionic strength, which varies between different aggregation protocols during in vitro drug screenings. In this work, we examine the interplay between ionic strength and a well-known protein aggregation inhibitor—epigallocatechin-3-gallate. We show that changes in solution ionic strength have a major impact on the compound’s inhibitory effect, reflected in both aggregation times and final fibril structure. We also observe that this effect is unique to different amyloid-forming proteins, such as insulin, alpha-synuclein and amyloid-beta.

Zone Head Selection Algorithm Based on Fuzzy Logic for Wireless Sensor Networks

Clustering extends energy resources, improves scalability and preserves communication bandwidth of the network. Clustering is either categorized as static and dynamic or as equal and unequal. Hot-spots issue needs a high overhead and is prone to connectivity problems in the wireless sensor network and this can be only possible because of unequal clustering. In this paper a zone divisional method based on fuzzy logic has been proposed. This method uses a fuzzy logic to form clusters and allot nodes to them for the reduction of energy consumption, and extends the age of the sensor network. The simulation and results section shows that the outperformance of the proposed algorithm, where the (EAUCF) energy-aware unequal clustering fuzzy, (LEACH) lowenergy adaptive clustering hierarchy, (EAMMH) energy-aware multi-hop multi-path hierarchical, and (TTDFP) two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multi-hop wireless sensor networks algorithms. The proposed algorithm has better results in terms of energy consumption minimization, load balancing, and prolongation of the network lifetime.

Privacy-enhancing distributed protocol for data aggregation based on blockchain and homomorphic encryption

The recent increase in reported incidents of security breaches compromising users' privacy call into question the current centralized model in which third-parties collect and control massive amounts of personal data. Blockchain has demonstrated that trusted and auditable computing is possible using a decentralized network of peers accompanied by a public ledger. Furthermore, Homomorphic Encryption (HE) guarantees confidentiality not only on the computation but also on the transmission, and storage processes. The synergy between Blockchain and HE is rapidly increasing in the computing environment.This research proposes a privacy-enhancing distributed and secure protocol for data aggregation backboned by Blockchain and HE technologies. Blockchain acts as a distributed ledger which facilitates efficient data aggregation through a Smart Contract. On the top, HE will be used for data encryption allowing private aggregation operations. The theoretical description, potential applications, a suggested implementation and a performance analysis are presented to validate the proposed solution.

A Novel Cluster Head Selection and Data Aggregation Protocol for Heterogeneous Wireless Sensor Network

A Novel Cluster Head Selection and Data Aggregation Protocol for Heterogeneous Wireless Sensor Network

Fuzzy Logic Based Zone Head Selection Algorithm for Wireless Sensor Networks

Hot-spots are a problem that comes in the cluster-based routing protocol that employs multi-hop communication due to this problem the energy among the sensor nodes is not balanced. The hot-spots issue requires high overhead and is prone to connectivity issues in the sensor network this can be only possible because of unequal clustering. In this method, we have to act on all the nodes of the sensor network for communication. This process consumes high system energy if the numbers of nodes are very high. To offer guaranteed connectivity, decrease high usage and complexity, a fuzzy logic-based zone divisional method has been proposed in this paper. Use fuzzy logic to create clusters and assign nodes to them to decrease the consumption of energy and the age of the network prolongation. The simulation and results section shows the outperformance of the proposed protocol, where the (LEACH) low-energy adaptive clustering hierarchy, (EAUCF) energy-aware unequal clustering fuzzy,(EAMMH) energy-aware multi-hop multi-path hierarchical, and (TTDFP) two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multi-hop wireless sensor networks algorithms. The proposed algorithm has better results in terms of energy consumption minimization, and prolongation of the network lifetime.

An Improved D-SMART Algorithm for Wireless Sensor Networks

In this paper, a secure and low energy dynamic slicing algorithm, namely, Improved D-SMART (IM-D-SMART) based on the Data Aggregation Protocol on Slice Mix Aggregate (D-SMART) is proposed to improve the security and confidentiality of wireless sensor networks and reduce the energy consumption of nodes in data collection and transmission in wireless sensor networks. According to the importance of data, the residual energy of nodes and the relative density of nodes, the data are dynamically partitioned to improve the D-SMART algorithm. Simultaneously, sending negative number splicing is used to compensate for the loss caused by the collision of data transmission between nodes. The simulation results show that IM-D-SMART outperforms D-SMART in terms of computation, privacy and communication cost.

A novel appliance-based secure data aggregation scheme for bill generation and demand management in smart grids

ABSTRACT Internet of Things (IoT) has been introduced into smart grids, which has achieved great improvement. The statistics of power consumption is one of the important functions but could lead to the leakage of user daily behaviour. Researchers have put efforts into secure data aggregation protocols to avoid such potential risk. However, only a few schemes have considered the dynamic unit price of electricity, and no schemes have been designed for calculating the power consumption of every appliance in a specific area. This paper proposes a novel appliance-based data aggregation scheme (ABDAS) for bill generation and demand management in smart grids. In the proposed scheme, chameleon hash function (CHF) is utilised to facilitate the extraction of aggregated data due to the characteristic of collision controllability. Furthermore, indistinguishability obfuscation (IO) is utilised to keep the chameleon hash value secret and decrease the overhead of the trusted third party. The fog nodes (FNs) in our scheme are responsible for the calculation of aggregation with its powerful computing and storage capabilities. The security analysis shows that our scheme satisfies IND-CPA and multiple security goals. Additionally, the performance evaluation indicates that the computational overhead of our scheme is lower than that of existing schemes.

Throughput Maximization Based on Optimized Frame-Aggregation Levels for IEEE 802.11 WLANs

To improve the throughput via frame aggregation (FA), the number of aggregate medium access control service data unit (A-MSDU) and protocol data unit (A-MPDU) subframes must be set appropriately under the IEEE 802.11 standards. In this letter, we construct an optimization problem that maximizes theoretical throughput considering bit error in A-MSDU under the IEEE 802.11ac standard based on the detailed structure of FA. Further, we propose a scheme to determine the optimal subframe sets through an exhaustive search for not only A-MPDU but also A-MSDU. The proposed scheme is validated through numerical calculations and Monte Carlo computer simulations. Although the computational complexity of the proposed scheme is larger than that of the conventional scheme, it leads to a significant throughput improvement.

A Privacy-Preserving Homomorphic Scheme With Multiple Dimensions and Fault Tolerance for Metering Data Aggregation in Smart Grid

Advanced Metering Infrastructure (AMI) facilitates the communication between smart meters and network operators in smart grid. For better demand-response management, smart meters are supposed to send live or sometimes periodic consumption reports. If such reports are intercepted or eavesdropped by a malicious entity, customers’ privacy is compromised, since vital information can be inferred from power consumption data. In this article, we propose a novel homomorphic privacy-preserving protocol (called NHP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for data aggregation that has a low computational cost compared to its rivals. It is fault-tolerant, supports multi-category aggregation, and can do batch verification at the intermediate aggregator as well as the central system. The proposed protocol is secure even when the gateway or aggregator turns malicious. It does not allow any compromised meter to find other users’ consumption information either. Moreover, in this scheme, the central system cannot infer any usage data even if it is curious and gains access to the data packets sent from meters to the intermediate aggregator. A comprehensive and comparative analysis is carried out at the end of this article which shows the advantages of the proposed scheme in terms of security features and cost.

An Unequally Clustered Multi-hop Routing Protocol Based on Fuzzy Logic for Wireless Sensor Networks

Wireless sensor networks (WSNs) is associated with a new exemplification of a real-time embedded system used for various applications that make the traditional infrastructure-based network seem infeasible. Due to imbalanced energy consumption among nodes, WSN has challenges with better utilization of energy and system enhancement. Clustering has been a better approach in this sequence. Therefore, this paper will utilize a fuzzy logic-based clustering protocol (unequal clustering) with multi-hop transmission for load balancing, energy consumption minimization, and network lifetime prolongation. The protocol forms unequal clusters with cluster head (CH) being selected by fuzzy logic with competition radius. Node distance to the base station, concentration, and residual energy are input variables. The simulation and results section displays the outperformance of the proposed protocol, where the low-energy adaptive clustering hierarchy (LEACH), energy-aware multi-hop multi-path hierarchical (EAMMH), energy-aware unequal clustering fuzzy (EAUCF), and two-tier distributed fuzzy logic-based protocol (TTDFP) for efficient data aggregation in multi-hop wireless sensor networks algorithms.

A Comparative Analysis of Data Aggregation Based Routing Protocols Hierarchies and Comparing Secure Data Aggregation Protocols in Wireless Sensor Networks

Sensor nodes deployed in Wireless Sensor Networks (WSNs) are battery powered. It operates at low voltage. The main challenge in WSNs is to reduce energy consumption so that it increases the lifetime of the sensor nodes. The goal is achieved by using data aggregation technique and also by selecting the appropriate routing strategy. Sensor nodes are commonly arranged in unmanned location; the security of data plays another important role. In order to provide reliable data to the destination, there are various approaches designed for security. In this paper, we analyzed the various existing approaches for data routing, data aggregation and data security.

Identity-Based Verifiable Aggregator Oblivious Encryption and Its Applications in Smart Grids

Verifiable aggregator oblivious encryption is a useful notion that not only allows an untrusted aggregator to compute aggregated data without learning any other information but also requires the aggregator to generate a proof of the aggregated data so that anyone can verify the accuracy. Since the aggregator does not learn data that may reveal users' private information and also cannot forge the aggregated data, verifiable aggregator oblivious encryption is suitable for privacy-preserving smart metering. However, NIST has recommended not using public-key-based cryptography in the smart grid because public-key cryptography cannot provide sufficient scalability. Thus, we propose a notion of identity-based verifiable aggregator oblivious encryption and design concrete schemes in this paper. We prove the aggregator oblivious security and aggregator unforgeability of our schemes via the smooth projective hash function and computational Diffie-Hellman assumption. Furthermore, based on our identity-based verifiable aggregator oblivious encryption scheme, we propose an identity-based data aggregation protocol for the smart grid and provide a security analysis in the context of seven typical attacks on smart grids. The implementation of our protocol via the Intel Edison platform shows that it is sufficiently lightweight for resource-constrained smart meters.

Privacy-Utility Equilibrium Protocol for Federated Aggregating Multiparty Genome Data

Cloud server aggregates a large amount of genome data from multi genome donors to facilitate scientific research. However, the untrusted cloud server is prone to violate privacy of aggregating genome data. Thus, each genome donor can randomly perturb her genome data using differential privacy mechanism before aggregating. But this is easy to lead to utility disaster of aggregating genome data due to the different privacy preferences of each genome donor, and privacy leakage of aggregating genome data because of the kinship between genome donors. The key challenge here is to achieve an equilibrium between privacy preserving and data utility of aggregating multiparty genome data. To this end, we proposed federated aggregation protocol of multiparty genome data (MGD-FAP) with privacy-utility equilibrium for guaranteeing desired privacy protection and desired data utility. First, we regarded the privacy budget and the accuracy as the desired privacy-utility metrics of genome data respectively. Second, we constructed the federated aggregation model of multiparty genome data by combining random perturbation method of genome data guaranteeing desired data utility with federated comparing update method of local privacy budget achieving desired privacy preserving. Third, we presented the MGD-FAP maintaining privacy-utility equilibrium under the federated aggregation model of multiparty genome data. Finally, our theoretical and experimental analysis showed that MGD-FAP can maintain privacy-utility equilibrium. The MGD-FAP is practical and feasible to ensure the privacy-utility equilibrium of cloud server aggregating multiparty genome data.

Comparative Analysis on Energy-Efficient Data Aggregation Methods in Wireless Sensor Network

The powerful impact of Wireless Sensor Networks has made them popular in different application areas such as health monitoring, military surveillance, environment monitoring, etc. Over a decade, significant growth has been made in the area however, there are several challenges in the design and successful deployment of Wireless Sensor Networks for end-to-end applications.The article presents a comparative analysis of the techniques used to improve the performance of a wireless sensor network by using different data aggregation protocols. Amongst all existing approaches of data aggregation, the most important approach is based on the formation of Clusters of nodes. As all nodes do not have the same level of energy hence efficient clustering is necessary to conserve the power of each node. The architecture of wireless sensor networks (WSNs) can be homogeneous or heterogeneous. It has been observed that different mechanisms exist for classification, data aggregation, and optimization of the energy in each node. The study compares the performance of different wireless sensor networks based on different parameters and subsequently analyzes how such schemes clash with particular requirements. Lastly, this study examines the techniques employed in the WSNs to reach a trade-off among different needs and reflect comparisons in energy utilization of traditional existing data aggregation methods.

A Privacy-preserving Multi-dimensional Data Aggregation Scheme with Forward Security in Smart Grid

Smart grid is a new generation of power system on the strength of rapid-speed bidirectional communication network. In smart grid, the electricity data from users needs to be collected to realize efficient energy management, which may reveal their privacy. In the past period of time, data aggregation protocols have been extensively studied to solve this problem. However, these protocols treat users’ electricity data as single-dimensional data, which is inconsistent with reality. Moreover, key leakage is also a serious security threat in the smart grid. Therefore, we propose a multi-dimensional data aggregation protocol with forward security. In this protocol, a cube data structure is designed to represent the multi-dimensional electricity consumption data of users in a region over multiple time periods, realizing the dual aggregation of electricity data both in time and space. In addition, security analysis shows that the proposed protocol satisfies multiple security properties and performance analysis shows that our protocol is more superior in the smart grid environment.

Particle Swarm Optimization-Based Data Aggregation in Wireless Sensor Network

Wireless sensor networks have battery-operated sensor nodes, which need to be conserved to have prolonged network lifetime. The amount of power consumed for routing sensed data from the sensor node to the sink node is large. Thus, in order to optimize the energy usage in sensor network efficient data aggregation techniques are needed. Particle swarm optimization (PSO) is a speculative and evolutionary computing technique based on swarm intelligence for solving optimization problems in sensor network such as nodes deployment, node scheduling, data clustering, and aggregation. The paper proposes a PSO-based sensor network aggregation protocol (PSO-SNAP) with K-means to provide initial centroid. The PSO has been used to find the optimal aggregated value having minimum quantization error. The output of the K-means algorithm is used as an initial centroid in PSO. Apart from K-means, K-medoid and simple average has also been used to provide initial seed to the PSO algorithm and results of all three approaches are compared.

Efficient data management for urban logistics distribution

At present, the urban population accounts for an increasing proportion of the total population, and the dense urban population has also caused some problems. To solve the issue of delayed material delivery, urban logistics distribution has become one of the key solutions. With the rapid improvement of sensor nodes and wireless technique, time-series data applications become increasingly popular. In an urban logistics distribution application, the vehicles can be the sensing user while the statistics data user is the data aggregator. In many cases, the aggregator is untrusted which makes it much more difficult to protect sensing users' privacy. In this paper, we take the multi-threads smart devices into consideration, and propose a privacy-preserving protocol which largely decreases decryption computation overheads. After importing multi-threads computation, we enhance a scalable privacy-preserving data aggregation protocol to make it less time costly and more suitable for large sensing system.

VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning

Federated learning (FL) enables a large number of clients to collaboratively train a global model through sharing their gradients in each synchronized epoch of local training. However, a centralized server used to aggregate these gradients can be compromised and forge the result in order to violate privacy or launch other attacks, which incurs the need to verify the integrity of aggregation. In this work, we explore how to design communication-efficient and fast verifiable aggregation in FL. We propose V eri FL, a verifiable aggregation protocol, with $O(N)$ (dimension-independent) communication and $O(N+ d)$ computation for verification in each epoch, where $N$ is the number of clients and $d$ is the dimension of gradient vectors. Since $d$ can be large in some real-world FL applications (e.g., 100K), our dimension-independent communication is especially desirable for clients with limited bandwidth and high-dimensional gradients. In addition, the proposed protocol can be used in the FL setting where secure aggregation is needed or there is a subset of clients dropping out of protocol execution. Experimental results indicate that our protocol is efficient in these settings.

Private Aggregation With Application to Distributed Optimization

This letter presents a fully distributed private aggregation protocol that can be employed in dynamical networks where communication is only assumed on a neighbor-to-neighbor basis. The novelty of the scheme is its low overhead in communication and computation due to a pre-processing phase that can be executed even before the participants know their input to aggregation. Moreover, the scheme is resilient to node drop-outs, and it is defined without introducing any trusted or untrusted third parties. We prove the privacy of the scheme itself and subsequently, we discuss the privacy leakage caused by the output of the scheme. Finally, we discuss implementation of the proposed protocol to solve distributed optimization problems using two versions of the alternating direction method of multipliers (ADMM).