Aggregation Scheme Research Articles

An ADP framework for flexibility and cost aggregation: Guarantees and open problems

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on Approximate Dynamic Programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation.

More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning

More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning

Developing and validating a knowledge-based AI assessment system for learning clinical core medical knowledge in otolaryngology

BackgroundClinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning). ObjectivesThis study aims to develop a multi-expert knowledge–aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital. MethodsThe MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire. ResultsBoth UPGY (z = −3.976, P < 0.001) and ENT-R (z = −2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = −1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = −4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales. ConclusionsThe MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.

PFCAS—Paring free certificate less aggregate scheme for ensuring efficient authentication in vehicular Ad-hoc networks

PFCAS—Paring free certificate less aggregate scheme for ensuring efficient authentication in vehicular Ad-hoc networks

An efficient privacy-preserving and verifiable scheme for federated learning

As one of the most important methods of privacy computing, federated learning has attracted much attention as it makes data available but invisible (i.e., uploading gradients instead of raw data). However, adversaries may still recover some private information such as tabs, memberships or even training data, from gradients. Additionally, the malicious server may return the incorrect or forged aggregated result to clients for certain illegal interests. To ensure verifiability and privacy-preservation, in this paper, we present a verifiable secure aggregation scheme under the dual-server federated learning framework. Specifically, we combine the learning with error (LWE) cryptosystem with the secret sharing technique to guarantee the privacy of the aggregated result and each client’s local gradient. Meanwhile, we skillfully design a double-verification protocol, including the server-side and client-side verification, to efficiently verify the correctness of the aggregated result and ensure data availability. Specifically, two servers mutually verify the correctness of the aggregated result through the linear homomorphic hash technique. After passing the server-side mutual verification, the malicious server may still directly broadcast the forged aggregated result to clients. Our client-side verification protocol can ensure data availability to identify the correct aggregation result sent by the semi-trusted server. To the best of our knowledge, existing solutions do not take data availability into account. Extensive experimental comparisons with the state-of-the-art schemes demonstrate the effectiveness and efficiency of the proposed scheme in terms of accuracy, computational cost and communication overhead.

Secure multi-party computation with secret sharing for real-time data aggregation in IIoT

Real-time analytics in Industrial Internet-of-Things (IIoT) has received remarkable attention recently due to its capacity to prevent downtime and manage risks. However, the sensed data in IIoT is considered private. Thus, the sensed data of IIoT nodes cannot be transmitted and utilized directly in the cloud server due to the risk of privacy leakage. Data aggregation can effectively balance the availability of data with privacy concerns, making it particularly well-suited for IIoT systems. Although several privacy-preserving aggregation schemes in IIoT have been proposed, the majority of them can only support a single type of aggregation that limits the application scenarios of data aggregation. To address the problems mentioned above, a real-time aggregation analysis scheme for IIoT is proposed, which is constructed based on secure multi-party computation with secret sharing. Specifically, the multi-party computation with secret sharing is utilized to implement data aggregation process for IIoT that achieves multiple types of data aggregation. In addition, the secret sharing is utilized in the proposed scheme that can significantly improve the efficiency of the proposed scheme compared with similar schemes. Moreover, the proposed scheme does not require the involvement of a trusted authority in the data aggregation. Security and performance analyses show that the proposed scheme can enhance the security of the sensed data while effectively aggregating data for real-time analytics in IIoT.

Smart contract assisted secure aggregation scheme for model update in federated learning

Federated learning (FL) is a cutting-edge machine learning architecture that effectively meets the data and model training requirements of numerous businesses, while ensuring privacy protection, data security, and adherence to legal constraints. However, recent studies have demonstrated that attackers can deduce users’ personal data from their shared model parameters, thus underscoring a substantial security risk for federated learning. Despite considerable efforts by academics to address this issue, current approaches suffer from drawbacks such as high communication and processing costs, insufficient robustness, and heavy reliance on trusted third parties. In this paper, we present the smart contract assisted secure aggregation scheme (SCSA) to overcome the aforementioned problems. First, we creatively offer a three-layer secure aggregation architecture that is particularly suitable for scenarios involving multiple sensors used in model training. Secondly, to ensure the credibility of the identities between participating entities in the model parameters aggregation process of FL, a novel certificateless authentication mechanism relying on certificateless encryption is provided. Moreover, for the sake of enhancing the security of the aggregation process in FL, we employ a combination of smart contracts and secret sharing techniques to distribute security masks to users in a decentralized and highly secure manner. It also combines with secure communication to create a double fault tolerance mechanism that significantly boosts the whole system’s robustness. The effectiveness of our approach in achieving secure aggregation is substantiated through theoretical analysis and simulated trials, while simultaneously ensuring strong security and robustness.

A New Framework of Collaborative Learning for Adaptive Metric Distillation.

This article presents a new adaptive metric distillation approach that can significantly improve the student networks' backbone features, along with better classification results. Previous knowledge distillation (KD) methods usually focus on transferring the knowledge across the classifier logits or feature structure, ignoring the excessive sample relations in the feature space. We demonstrated that such a design greatly limits performance, especially for the retrieval task. The proposed collaborative adaptive metric distillation (CAMD) has three main advantages: 1) the optimization focuses on optimizing the relationship between key pairs by introducing the hard mining strategy into the distillation framework; 2) it provides an adaptive metric distillation that can explicitly optimize the student feature embeddings by applying the relation in the teacher embeddings as supervision; and 3) it employs a collaborative scheme for effective knowledge aggregation. Extensive experiments demonstrated that our approach sets a new state-of-the-art in both the classification and retrieval tasks, outperforming other cutting-edge distillers under various settings.

RSDA: A Reliable and Secure Data Aggregation Scheme for Smart Grid

Smart grids are modern power distribution systems that utilize advanced technologies to optimize energy generation, distribution, and consumption. The huge amount of data generated by various sources, such as smart meters, sensors, and other devices, can be aggregated for efficient energy management, supporting demand response etc. However, this data can contain sensitive information about individual’s energy usage patterns, behaviour, and preferences, which raises several privacy concerns. Data reliability is another major issue, as the data send from the smart meters to the aggregators may get corrupted due to errors introduced by the communication channel. In this paper, a private-key additive homomorphic encryption scheme based on polar codes is proposed for privacy preserving data aggregation. The proposed scheme not only ensures data confidentiality but also provide data reliability. The security of this scheme relies on the difficulty in decoding the codeword and is achieved by embedding security in the generator matrix and error vector. Security analysis shows that the proposed scheme is resistant against all known attacks against private-key code-based cryptosystems. The performance analysis is evaluated in terms of error performance, communication overhead and computational complexity. The performance comparison with related additive homomorphic encryption scheme shows the efficiency of the proposed scheme.

Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland

Ireland experiences high energy poverty rates alongside surplus wind energy resources. With 77% of Irish households equipped with electrical immersion heaters for domestic hot water (DHW) generation, this study proposes an Electrical Water Heating Aggregation (EWHA) scheme. The scheme allocates surplus wind-generated electricity to provide DHW to fuel-poor households, thereby alleviating energy poverty through harnessing curtailed wind energy. Through a developed wind-generated electricity allocation model and half-hourly data analysis for a weather year, this research assesses the feasibility and economic viability of the EWHA scheme, focusing on the householder as the primary benefactor from the scheme (as opposed to ancillary grid service provision). The results suggest an optimal aggregation size where maximum curtailment and carbon offset coincide with maximum benefits for participants. The findings indicate that fuel-poor households in Ireland could receive a full DHW tank every three weeks using surplus wind energy, harnessing 89% of overnight curtailed wind energy and offsetting 33 MkgCO2 annually. Moreover, the scheme could potentially save the Irish state approximately EUR 4 million by 2030, increasing to EUR 11 million by 2050, in carbon costs. Overall, this research demonstrates the potential of EWHA schemes to alleviate energy poverty, optimise wind energy utilisation, and contribute significantly to carbon emission reduction targets.

IPCAS: An improved conditional privacy-preserving certificateless aggregate signature scheme without bilinear pairing for VANETs

Recently, Gong et al. proposed a Certificateless Aggregate Signature (CLAS) scheme (shorted by PCAS) with conditional privacy-preserving without bilinear pairing for Vehicular Ad-hoc Networks (VANETs). Unfortunately, after analyzing security, the PCAS scheme fails to satisfy the necessary unlinkability in VANETs and is vulnerable to the public key replacement forgery attack of the Type-I adversary. Subsequently, this article analyzes the causes of these two security issues in detail and proposes an improved CLAS scheme for VANETs (called IPCAS). Through security analysis, this article proves that the IPCAS is existentially unforgeable under the adaptive chosen message attacks against Type-I and Type-II adversaries in the random oracle model, and satisfies the necessary security and privacy requirements in VANETs, including unlinkability. Finally, the performance analysis results show that compared to PCAS for single message and n aggregate messages (n=1000), the computational overhead of IPCAS is reduced by 20.01% and 49.08% respectively, and the communication overhead is reduced by 18.75% and 28.14% respectively. Therefore, IPCAS not only makes up for the security vulnerabilities of PCAS but also has better performance.

VMEMDA: Verifiable Multidimensional Encrypted Medical Data Aggregation Scheme for Cloud-Based Wireless Body Area Networks

VMEMDA: Verifiable Multidimensional Encrypted Medical Data Aggregation Scheme for Cloud-Based Wireless Body Area Networks

Privacy‐preserving data aggregation achieving completeness of data queries in smart grid

AbstractIn smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine‐grained data. To address these challenges, we propose a privacy‐preserving data aggregation scheme supporting data query (PAQ). We designed a multi‐level data aggregation mechanism based on Paillier semi‐homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency.

On the Number of Customer Classes in a Single-Period Inventory System

A common practice in inventory systems with several customers requiring differentiated service levels is to group them into two or three classes, where a customer class is a group of customers with the same preset service level in terms of product availability. However, there is no evidence that grouping customers into two or three classes is optimal in terms of the ordering policy parameters. This paper studies the effect of the number of customer classes on the inventory level of a single-period inventory system with stochastic demand and individual service-level requirements from multiple customer classes. Using a Sample Average Approximation approach, we formulate computationally tractable multi-class service level models, under responsive and anticipative priority policies in cases of shortage, as mixed integer linear problems (MIPs). The effect of the number of classes on the inventory level is determined using a round-up aggregation scheme; i.e., given a sufficiently large initial number of classes, it is reduced by adding the lower service level classes to the next higher class. We analytically characterize the optimal inventory level under responsive and anticipative priority policies as a function of the initial number of classes and the number of classes grouped based on the round-up aggregation scheme. Under a responsive priority policy, we show that there is an optimal number of classes, while under an anticipative priority policy, the optimal number of classes is equal to the initial number of classes. The effect of free-riders resulting from the round-up aggregation scheme on the optimal inventory level is studied through numerical experiments.

Learning spatiotemporal dependencies using adaptive hierarchical graph convolutional neural network for air quality prediction

Air quality prediction has garnered significant attention from both governmental bodies and the general public due to its close connection with people’s daily lives. Learning spatio-temporal dependencies within air quality data are crucial for accurate air quality forecasting. Graph Convolutional Network (GCN) has been widely utilized in spatiotemporal data prediction due to its remarkable ability of capturing non-Euclidean spatial dependencies among nodes. However, previous research on Spatiotemporal Graph Neural Networks (ST-GNNs) has either utilized graph adjacency matrices predefined by heuristic rules or learned through trainable parameters. These approaches fail to accurately represent the true spatial relationships between stations and adaptively capture the required multi-level spatial dependencies for prediction. To address these limitations, this paper proposes an Adaptive Hierarchical Graph Convolutional Neural Network (AHGCNN) for PM2.5 prediction. More specially, an adaptive hierarchical graph convolutional structure is initially developed for dynamically extracting multi-level spatial dependencies. Then, a hierarchical aggregation scheme is designed to consolidate the unique topological structures of graph neural networks across different levels. Finally, a unified Adaptive Hierarchical Graph Convolution Gated Recurrent Unit (AHGC-GRU) model is proposed by integrating Gated Recurrent Unit (GRU) with hierarchical graph convolution in an effort to capture spatiotemporal dependencies of air quality data. Extensive experiments are conducted on real-world datasets, and the results demonstrate that the proposed model outperforms existing models.

MA-VLAD: a fine-grained local feature aggregation scheme for action recognition

MA-VLAD: a fine-grained local feature aggregation scheme for action recognition

Balancing data privacy and sharing in IIoT: Introducing the GFL-LFF aggregation algorithm

As the Industrial Internet of Things (IIoT) continues to evolve, the need for effective data aggregation schemes that balance the imperatives of data sharing and individual data privacy becomes paramount. Traditional data aggregation methods often entail risks of sensitive information exposure during data transmission and storage. This paper proposes a novel Graphical Federated Learning-based Local Fennec Fox (GFL-LFF) algorithm to ensure secure aggregation by minimizing the revelation of individual data. In this study, a Graph Neural Network (GNN) with federated learning is employed to enhance privacy protection for IIoT's data aggregation scheme. A Local search-based fennec fox optimization is utilized for tuning the parameters of GNN. The GFL and LFF approaches are integrated to enhance the security of the data that can be shared through the IIoT system. The MNIST dataset, CIFAR-10 dataset, and LFW dataset were taken to conduct the experimentation results, and the evaluation measures, such as throughput, end-to-end delay, network lifetime, latency, and energy consumption, are utilized to evaluate the proposed GFL-LFF method, and these results are compared with other approaches. The GFL-LFF method achieved a throughput of 0.98 Mbps, an end-to-end delay of 1.2 s, a network lifetime of 5610 rounds, a latency of 1.6 s, and an energy consumption of 0.2 mJ. The experimental results illustrate the effectiveness of the proposed GFL-LFF method for IIoT's data aggregation scheme in privacy protection.

EPFL-DAC: Enhancing Privacy in Federated Learning with Dynamic Aggregation and Clipping

Federated learning achieves data privacy protection by uploading only users’ local updates. However, recent research has shown that privacy data can still be inferred from local updates. Privacy-preserving federated learning also faces challenges related to the confidentiality and privacy of local updates, the robustness of dynamic membership schemes, and the determination of appropriate clipping threshold in differentially private federated learning. To address these issues, this paper presents the EPFL-DAC scheme: Enhancing Privacy in Federated Learning with Dynamic Aggregation and Clipping. Firstly, Paillier homomorphic encryption is employed to securely aggregate local updates, ensuring that the server can only access the aggregated information. Secondly, a dynamic robustness algorithm is designed to ensure the robustness of the aggregation scheme when user states change. Lastly, a dynamic threshold determination method is introduced to introduce more accurate noise, while also providing resistance against collusive attacks in differentially private settings. Experimental results demonstrate the effectiveness, low computational overhead, and preservation of accuracy in the dynamic aggregation scheme. Additionally, the proposed dynamic threshold determination method exhibits good performance, especially at higher privacy protection levels and larger initial clipping threshold values.

On the Security of Lightweight and Escrow-Free Certificate-Based Data Aggregation for Smart Grid

Recently, to eliminate complex certificate maintenance and key escrow issue, Verma <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed the first certificate-based(CB-based) data aggregation scheme for smart grids, in which a lightweight CB-based signature is proposed to ensure the integrity of the transmitted data. Although they claimed that the proposed certificate-based signature scheme is secure against attacks by a malicious certification authority (CA), unfortunately, by analyzing the security of Verma <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> 's scheme, we show that their scheme is insecure. It can not provide data integrity since a malicious CA can forge a message's signature in the name of any entity. After giving the corresponding attacks, we analyze the reasons for producing such attacks and provide the corresponding suggestions to overcome them.

A Privacy Protection Scheme of Certificateless Aggregate Ring Signcryption Based on SM2 Algorithm in Smart Grid

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, the increase in users will also lead to more security problems, and it is also necessary to solve the privacy protection for users during information transmission. In order to solve the above problems, this paper proposes an aggregated ring encryption scheme based on the SM2 algorithm with special features, referred to as SM2-CLARSC, based on the certificateless ring signcryption mechanism and combining with the aggregate signcryption. SM2-CLARSC is designed to satisfy the basic needs of the smart grid, and it can be resistant to replay attacks, forward security and backward security, etc. It has better security and higher efficiency than existing solutions. Comparing SM2-CLARSC with existing typical solutions through simulation, the result proves that this solution has more comprehensive functions, higher security, and significant computational efficiency improvement.