Privacy-preserving Aggregation Research Articles

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability

Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability

A Privacy-Preserving Aggregation Scheme With Continuous Authentication for Federated Learning in VANETs

A Privacy-Preserving Aggregation Scheme With Continuous Authentication for Federated Learning in VANETs

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

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.

Efficient and Privacy-Preserving Aggregate Query Over Public Property Graphs

Efficient and Privacy-Preserving Aggregate Query Over Public Property Graphs

An Efficient Multi-Party Secure Aggregation Method Based on Multi-Homomorphic Attributes

The federated learning on large-scale mobile terminals and Internet of Things (IoT) devices faces the issues of privacy leakage, resource limitation, and frequent user dropouts. This paper proposes an efficient secure aggregation method based on multi-homomorphic attributes to realize the privacy-preserving aggregation of local models while ensuring low overhead and tolerating user dropouts. First, based on EC-ElGamal, the homomorphic pseudorandom generator, and the Chinese remainder theorem, an efficient random mask secure aggregation method is proposed, which can efficiently aggregate random masks and protect the privacy of the masks while introducing secret sharing to achieve tolerance of user dropout. Then, an efficient federated learning secure aggregation method is proposed, which guarantees that the computation and communication overheads of users are only O(L); also, the method only performs two rounds of communication to complete the aggregation and allows user dropout, and the aggregation time does not increase with the dropout rate, so it is suitable for resource-limited devices. Finally, the correctness, security, and performance of the proposed method are analyzed and evaluated. The experimental results indicate that the aggregation time of the proposed method is linearly related to the number of users and the model size, and it decreases as the number of dropped out users increases. Compared to other schemes, the proposed method significantly improves the aggregation efficiency and has stronger dropout tolerance, and it improves the efficiency by about 24 times when the number of users is 500 and the dropout rate is 30%.

PREEN: An Aggregation Mechanism for Privacy-Preserving Smart-Metering Communications

The widespread use of home consumer eletronics (CEs) provides residents facility, assistance and comfort. Meanwhile, smart meters are deployed at households to optimize electricity supply and demand and improve electricity efficiency. These meters monitor, collect and aggregate the electricity consumption of the home CEs in a near-real-time way. However, such processes seriously invade consumer privacy, e.g., household occupancy and economic status. Many existing privacy-preserving schemes rely on the assumption that certain components can be trusted, which is hard to be ensured, and cannot resist against collusion attacks. In this study, we leverage differential privacy and a re-encryption model to produce a privacy-preserving aggregation mechanism, called PREEN, for smart-metering communications. PREEN simultaneously facilitates data aggregation, differential privacy, and protection against various attacks. Specifically, we adopt ElGamal algorithm to construct the re-encryption scheme and differential privacy to aggregate the consumption measurements of residential users at the gateways and control center, thereby enabling resistance against slashing privacy attacks. Strict and detailed security proofs as well as extensive performance evaluations are adopted to demonstrate that the proposed PREEN provides rigorous privacy preservation and confidentiality against diverse external and internal attacks, while remaining a comparable or even lower computational overhead and a negligible communication overhead and a reasonable accuracy in terms of the utility of differential privacy, compared to the state-of-the-art mechanisms.

DScPA: A Dynamic Subcluster Privacy-Preserving Aggregation Scheme for Mobile Crowdsourcing in Industrial IoT

DScPA: A Dynamic Subcluster Privacy-Preserving Aggregation Scheme for Mobile Crowdsourcing in Industrial IoT

PrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices

AbstractValuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-devices like smartphones. To facilitate such an analysis, we present a toolchain called PrivAgE for a distributed, privacy-preserving aggregation of local data by taking the limited resources of edge-devices into account. The distributed aggregation is based on secure summation and simultaneously satisfies the notion of differential privacy. In this way, other parties can neither learn the sensitive data of single clients nor a single client’s influence on the final result. We perform an evaluation of the power consumption, the running time and the bandwidth overhead on real as well as simulated devices and demonstrate the flexibility of our toolchain by presenting an extension of the summation of histograms to distributed clustering.

Privacy-Preserving Aggregation in Federated Learning: A Survey

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. This survey reviews the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight significant challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.

An Efficient and Practical Conditional Privacy-Preserving Aggregate Authentication for Vehicular Ad-Hoc Networks

An Efficient and Practical Conditional Privacy-Preserving Aggregate Authentication for Vehicular Ad-Hoc Networks

FedChain-Hunter: A reliable and privacy-preserving aggregation for federated threat hunting framework in SDN-based IIoT

In the development of the Industrial Internet of Things (IIoT), cyber threats and attacks have become major issues and concerns in Industry 4.0 due to the negative impacts on the infrastructures and services across organizations. Nevertheless, due to the issues in preserving privacy and transparency, there is a lack of threat intelligence sharing among parties, leading to the low performance in uncovering malicious actors. In fact, the method of gathering and exploiting such data has been getting more crucial in a trend of machine learning (ML) adoption in cybersecurity. In this scenario, Federated Learning (FL) can assume a significant role in constructing an ML-based threat hunting solution for IIoT networks. This can be achieved by harnessing data resources from diverse parties, utilizing a local training strategy that eliminates the need for centralized data collection. Hence, this paper proposes FedChain-Hunter, a blockchain and FL-based threat-hunting framework to mutually seek cyber threats while ensuring data privacy and the transparency in the contribution of data owners. Specifically, Software Defined Networking (SDN) with programmable and flexible security orchestration is used to easily monitor and gather appropriate security events in the IIoT network. In addition, the Fully Homomorphic Encryption (HE) and Differential Privacy (DP) are integrated into the FL scheme to provide strong security and privacy-preserving aggregation for each ML model update. Also, the blockchain adoption offers the transparency, auditability for collaboration and contribution management through a decentralized platform. The experimental results on 5 datasets indicate that FedChain-Hunter can achieve high performance for cyber threat detection with security, reliability, and privacy guarantee.

Pyas: Pay for Your Aggregation Service—A Privacy Preserving Aggregation Protocol of Time-series Data

Pyas: Pay for Your Aggregation Service—A Privacy Preserving Aggregation Protocol of Time-series Data

Edge-Assisted Public Key Homomorphic Encryption for Preserving Privacy in Mobile Crowdsensing

Mobile crowdsensing (MCS) is becoming an increasingly important topic due to rapid proliferation of mobile apps where participants’ anonymity is a pivotal requirement with direct impacts on their safety and well being. There are two main challenges in crypto-based privacy-preserving aggregation in MCS, namely, participants joining or leaving the crowd randomly at will, and adversaries injecting fake data. The conventional approach for preserving privacy is to provide blanket anonymity to all, including adversaries, which enables the latter to cause harm without being identified. In addition, with the proliferation of edge servers, there is a need to develop edge-assisted MCS, which would be more efficient in terms of less back-haul traffic and less delay as compared to cloud-assisted-only MCS. In this paper, we present an efficient edge-assisted MCS scheme which preserves the participants’ privacy and anonymity, protects the service against adversaries, and can be used to verify that aggregation is free of anomalies. Our scheme is transparent to the join-and-leave problem; and its computational cost and communication overhead are small and fixed, i.e., it is insensitive to crowd count. We utilize group signature for source authentication to identify and block adversaries that cause harm while providing anonymity to ordinary participants.

Long-Term Privacy-Preserving Aggregation With User-Dynamics for Federated Learning

Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced federated learning (FL), which enables the server to obtain the sum of users’ locally trained models while keeping local training data private. However, most of the work on privacy-preserving aggregation provides privacy guarantees for only one communication round in FL. In fact, as FL usually involves long-term training, i.e., multiple rounds, it may lead to more information leakages due to the dynamic user participation over rounds. In this connection, we propose a long-term privacy-preserving aggregation (LTPA) protocol providing both single-round and multi-round privacy guarantees. Specifically, we first introduce our batch-partitioning-dropping-updating (BPDU) strategy that enables any user-dynamic FL system to provide multi-round privacy guarantees. Then we present our LTPA construction which integrates our proposed BPDU strategy with the state-of-the-art privacy-preserving aggregation protocol. Furthermore, we investigate the impact of LTPA parameter settings on the trade-off between privacy guarantee, protocol efficiency, and FL convergence performance from both theoretical and experimental perspectives. Experimental results show that LTPA provides similar complexity to that of the state-of-the-art, i.e., an additional cost of around only 1.04X for a 100,000-user FL system, with an additional long-term privacy guarantee.

Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning

With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention from both academia and industry. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients' gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature. Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to 6.37$\times$ in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.

Efficient Verifiable Protocol for Privacy-Preserving Aggregation in Federated Learning

Federated learning has gained extensive interest in recent years owing to its ability to update model parameters without obtaining raw data from users, which makes it a viable privacy-preserving machine learning model for collaborative distributed learning among various devices. However, due to the fact that adversaries can track and deduce private information about users from shared gradients, federated learning is vulnerable to numerous security and privacy threats. In this work, a communication-efficient protocol for secure aggregation of model parameters in a federated learning setting is proposed where training is done on user devices while the aggregated trained model could be constructed on the server side without revealing the raw data of users. The proposed protocol is robust against users’ dropouts, and it enables each user to independently validate the aggregated result supplied by the server. The suggested protocol is secure in an honest-but-curious environment, and privacy is maintained even if the majority of parties are in collusion. A practical scenario for the proposed setting is discussed. Additionally, a simulation of the protocol is evaluated, and results demonstrate that it outperforms one of the state-of-art protocols, especially when the number of dropouts increases.

Efficient ECC-based Conditional Privacy-preserving Aggregation Signature Scheme in V2V

Vehicular ad-hoc network system provides network communication technology and plays an important role in road safety and traffic control. Conditional privacy-preserving can safeguard privacy as well as trace malicious vehicles, which is critical in the system. However, in the restricted computing and communication resource situation, how to ensure the conditional privacy security remains a challenge. To address this issue, this research work proposes an efficient conditional privacy-preserving scheme. In this scheme, firstly, a certificateless aggregation signature has been performed on board to avoid channel conflicts, and to our best knowledge, this scheme is the first one to propose aggregation on board. Also an elliptic curve point multiplicative operation has been utilized to decrease the cost consumed in computation. Secondly, this paper gives the security proof of the proposed scheme against the existence of unforgeability under adaptively chosen message attack for Type-I and Type-II adversaries in the random oracle model. Lastly, comprehensive performance comparisons covering the computation, communication and storage costs have been done to state that the proposed scheme is more efficient than the existing solutions. Significantly, this scheme saves at least 86% in communication overhead than compared schemes.

Efficient privacy-preserving aggregation for demand side management of residential loads

An effective tool used in practice to maintain network balance is residential demand side management (DMS). However, privacy concerns related to the processing of user personal consumption data often result in a slow wide-scale adoption and acceptance. Various general purpose cryptographic techniques are available, such as homomorphic encryption and secure multi-party computation (MPC), to address these privacy-concerns. Unfortunately, these come at a significant computation price which makes them not applicable in many practical scenarios. In this work, a privacy-preserving aggregation algorithm, based on additive random shares and a combination of well-established symmetric and asymmetric key cryptography methods is proposed and compared with additive homomorphic encryption (AHE) techniques and state-of-the-art MPC protocols. The results show that generic techniques such as homomorphic encryption come with significant computational and communication cost, especially at the users side. On the other hand, MPC approaches provide better performance and resilience to dropouts for very large networks but entail a complex communication among users. In this context, the proposed additive random shares algorithm was the most balanced choice for DSM, with a good performance, simpler information flow and the possibility of adding redundant intermediary parties for enhanced resilience.