Privacy-preserving Data Aggregation Research Articles

Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

A secure paillier cryptosystem based privacy-preserving data aggregation and query processing models for smart grid

A secure paillier cryptosystem based privacy-preserving data aggregation and query processing models for smart grid

EFTA: An Efficient and Fault-Tolerant Data Aggregation Scheme without TTP in Smart Grid

Abstract With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes.

Verifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics

Verifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics

A Robust and Lightweight Privacy-Preserving Data Aggregation Scheme for Smart Grid

Privacy-preserving data aggregation (PPDA) enables data availability and privacy preservation simultaneously in smart grid. However, existing methods, such as masking and homomorphic encryption, cannot simultaneously offer strong privacy preservation, fault tolerance for both smart meters and aggregators, verifiable aggregation, and lightweight encryption. To tackle these challenges, we design HTV-PRE, a homomorphic threshold proxy re-encryption scheme with re-encryption verifiability. HTV-PRE involves only linear operations and resists quantum attacks after being instanced by ideal lattices. By leveraging HTV-PRE, we propose a robust and lightweight data aggregation scheme with strong privacy preservation for smart grid. Robustness ensures fault tolerance and error detection. Even if some smart meters or aggregators are faulty, data aggregation can still work without imposing expensive computation on other smart meters or requiring additional trust assumptions. Additionally, to detect aggregators' errors, a proof for the aggregated result is presented so that anyone can verify whether the result has been correctly computed or not. The verifiable aggregation adds no computation/communication overhead on the user side. The performance evaluations demonstrate that our PPDA scheme significantly offloads computation overhead from smart meters and control center to the edge, and its user encryption is up to 4x faster than existing approaches.

Smart Contract Assisted Privacy-Preserving Data Aggregation and Management Scheme for Smart Grid

Smart Contract Assisted Privacy-Preserving Data Aggregation and Management Scheme for Smart Grid

CoStricTor: Collaborative HTTP Strict Transport Security in Tor Browser

HTTP Strict Transport Security (HSTS) is a widely-deployed security feature in modern web browsing. It is also, however, a potential vector for user tracking and surveillance. Tor Browser, a web browser primarily concerned with online anonymity, disables HSTS as a result of this tracking potential. We present the CoStricTor protocol which crowdsources HSTS data among Tor Browser clients. It gives Tor Browser users increased resistance to man-in-the-middle attacks without exposing them to HSTS tracking. Our protocol adapts other privacy-preserving data aggregation algorithms to share data effectively among users with strong local differential privacy guarantees. The CoStricTor protocol resists denial of service attacks by design through our innovative use of Bloom filters to represent complementary data. Our simulations show our protocol can model up to 150,000 websites, providing 10,000 upgrades to HSTS for users.

EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme

Federated learning (FL) is a kind of privacy-aware machine learning, in which the machine learning models are trained on the users side and then the model updates are transmitted to the server for aggregating. As the data owners need not upload their data, FL is a privacy-persevering machine learning model. However, FL is weak as it suffers from a reverse attack, in which an adversary can get users data by analyzing the user uploaded model. Motivated by this, in this paper, based on the secret sharing, we design EPPDA, an efficient privacy-preserving data aggregation mechanism for FL, to resist the reverse attack, which can aggregate users trained models secretly without leaking the users model. Moreover, EPPDA has efficient fault tolerance [1] for the user disconnection. Even if a large number of users are disconnected when the protocol runs, EPPDA will execute normally. Analysis shows that the EPPDA can provide a sum of locally trained models to the server without leaking any single users model. Moreover, adversary can not get any non-public information from the communication channel. Efficiency verification proves that the EPPDA not only protects users privacy but also needs fewer computing and communication resources.

A Privacy-Preserving Data Aggregation Scheme Based on Chinese Remainder Theorem in Mobile Crowdsensing System

Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The generated data often contains the sensing node's sensitive information. Existing schemes dedicated to protecting the privacy of perception data, but these schemes cannot balance the computational and communication overheads well. Therefore, we propose a privacy-preserving data aggregation scheme based on the Chinese remainder theorem. Using blinding factor and Paillier homomorphic encryption technology, which not only ensures the privacy of perceived data but also improves the robustness of the system. Specially, the introduction of secure multicast communication technology based on the Chinese remainder theorem protects task privacy and ensures only designated sensing nodes can obtain the task. In addition, we design an efficient signature scheme to ensure data integrity. Detailed security analysis shows that our scheme is existentially unforgeable against adaptively chosen message attack. Extensive experiments and performance analysis show that our scheme is efficient and has a low communication overhead.

An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids

In the context of rapid advancements in artificial intelligence (AI) technology, new technologies, such as federated learning and edge computing, have been widely applied in the power Internet of Things (PIoT). When compared to the traditional centralized training approach, conventional federated learning (FL) significantly enhances privacy protection. Nonetheless, the approach poses privacy concerns, such as inferring other users’ training data through the global model or user-transferred parameters. In light of these challenges, this research paper introduces a novel privacy-preserving data aggregation scheme for the smart grid, bolstered by an improved FL technique. The secure multi-party computation (SMC) and differential privacy (DP) are skillfully combined with FL to combat inference attacks during both the learning process and output inference stages, thus furnishing robust privacy assurances. Through this approach, a trusted third party can securely acquire model parameters from power data holders and securely update the global model in an aggregated way. Moreover, the proposed secure aggregation scheme, as demonstrated through security analysis, achieves secure and reliable data aggregation in the electric PIoT environment. Finally, the experimental analysis shows that the proposed scheme effectively performs federated learning tasks, achieving good model accuracy and shorter execution times.

Differential privacy model for blockchain based smart home architecture

Secure and private communications using the Internet of Things (IoT) pose several challenges for smart home systems. In particular, data collected from IoT devices comprise sensitive personal information such as biomedical data, financial data, and location and activity data. Recent research looks into the use of blockchain in smart home systems, protecting the privacy of the data in use. Such solutions need to address the issue of privacy using a formal and mathematical model for data privacy due to the vulnerability associated with privacy-preserving blockchain networks. In the present paper, our approach aims to provide a privacy-preserving data aggregation mechanism in the context of Smart Homes that agree to contribute their data to a cloud server using machine learning to improve services for home users. We propose the use of differential privacy, a powerful concept in privacy preserving schemes to provide formal assurances about how much information is leaked using a privacy budget. The main purpose of using such a privacy-preserving scheme is to limit what can be inferred about individual training data from the model. Our techniques use a R′enyi differential privacy (RDP) machine learning scheme and are based on a variant of the stochastic gradient descent function. The performance of our proposed framework is evaluated using three public datasets: UNSW-NB15, NSL-KDD, and ToN-IoT datasets. Our findings show that differential private models can provide privacy protection against attackers by sacrificing a substantial amount of model utility. Therefore, we propose an empirical value of ϵ, that can optimally balances utility and privacy for the current smart home scenario datasets.

An Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grids in Cloud Environment

An Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grids in Cloud Environment

PPDA-FAF: Maintaining Data Security and Privacy in Green IoT-Based Agriculture

Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results.

IoT-based interaction design of smart home products for elderly families

Abstract A LoRa (Long Range Radio) based smart home product interaction system for the elderly is designed to solve the problem of reliability communication in the collection and information transmission of smart home environment parameters, device status parameters and the complex layout of communication lines inside smart devices. A privacy-preserving data aggregation scheme without a trusted authority center is proposed to address the privacy protection problem in the process of Internet of things (IoT) data collection. Firstly, without relying on the trusted authority center, the key exchange algorithm is used to generate shared secret keys and construct encryption keys among terminal devices to ensure the privacy of reported data; secondly, a fault tolerance mechanism is designed according to Carmichael’s theorem, so that the control center can still aggregate the received data when the terminal device fails or the network connection is wrong; in addition, the batch In addition, the integrity of the reported data is ensured by means of batch verification. Finally, the theoretical analysis proves the correctness and security of the scheme in this paper. The performance experiments show that the required computational overhead is lower compared to existing schemes, while some communication overhead is increased in order to have fault tolerance and to improve the verification efficiency of digital signatures. The application of this system will highlight the main position of the elderly, so that they can truly experience and accept the benefits of smart home for their lives and improve their quality of life.

Towards a Verification Approach of a Smart Registry for Chronic Heart Failure Patients.

The daily increasing amount of health data from different sources like electronic medical records and telehealth systems go hand in hand with the ongoing development of novel digital and data-driven analytics. Unifying this in a privacy-preserving data aggregation infrastructure can enable services for clinical decision support in personalized patient therapy. The goal of this work was to consider such an infrastructure, implemented in a smart registry for heart failure, as a comparative method for the analysis of health data. We analyzed to what extent the dataset of a study on the telehealth program HerzMobil Tirol (HMT) can be reproduced with the data from the smart registry. A table with 96 variables for 251 patients of the HMT publication could theoretically be replicated from the smart registry for 248 patients with 80 variables. The smart registry contained the tables to reproduce a large part of the information, especially the core statements of the HMT publication. Our results show how such an infrastructure can enable efficient analysis of health data, and thus take a further step towards personalized health care.

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

The tremendous growth of about 8 billion devices connected to each other in various domains of Internet of Things (IoT)-based applications have attracted researchers from both industry and academia. IoT is a network of several devices connected with each other to provide sensing capabilities, particularly in smart grid (SG) environment. Various challenges such as the efficient handling of massive IoT data can be addressed with advances in fog computing. The secure data aggregation challenge is one such issue in IoT-based smart grid systems, which include millions of smart meters. Typical SG-based data aggregation approaches have high computation and communication costs, however, many efforts have been made to overcome these limitations while leveraging fog computing but no satisfactory results have been obtained. Moreover, existing solutions also suffer from high storage requirements. The traditional data aggregation schemes such as GCEDA (Grouping of Clusters for Efficient Data Aggregation) and SPPDA (Secure Privacy-Preserving Data Aggregation) also suffer from a few shortcomings. SPPDA follows a mixed aggregation architecture that includes trees and clusters which can lead to some performance complexities and is not energy-efficient, whereas GCEDA does not support heterogeneity. To overcome these problems, this research provides a fog-assisted strategy for secure and efficient data aggregation in smart grid. The concept of smart grid is implemented in fog environment, which was not the case in previous schemes. We used communication between smart meters (SMs) and fog nodes (FNs) to transmit confidential data in compressed form towards FN. The FN further aggregates the received data which can then be updated in cloud repositories later. We presented two algorithms—data aggregation and data extraction at FN and cloud, respectively, to achieve secure communication. The performance of the proposed strategy has been evaluated against existing data aggregation techniques GCEDA and SPPDA for various performance parameters such as storage, communication cost and transmission cost. The proposed scheme overcomes the limitation of heterogeneity and mixed aggregation which was faced in GCEDA and SPPDA and the results revealed outstanding performance in comparison with both, so the proposed solution can be used in a smart grid environment for efficient and secure data transmission.

Privacy-preserving data aggregation scheme against deletion and tampering attacks from aggregators

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead.

A Robust and Optional Privacy Data Aggregation Scheme for Fog-Enhanced IoT Network

With the advances in fog computing, various users’ data, collected by smart devices in the Internet of Things (IoT), are published to facilitate services and efficiency. This leads to the users’ worry about security and privacy issues in fog-enhanced IoT. Although privacy protection can ease such worry, users need to pay some extra price, such as more computation costs. Besides, there are some users who want convenience rather than privacy. They prefer to publish their raw data to exchange for better convenience or benefit. In this article, an optional privacy-preserving data aggregation scheme based on BGN homomorphic encryption is proposed, without any trusted third party. In the proposed scheme, each user can choose either no privacy encryption or privacy encryption to upload its own data according to its own privacy sensitivity. With the help of the fog node, the control center can get the aggregated data of all smart devices and the single data of smart devices that choose the no privacy option. In addition, the proposed scheme is analyzed to achieve correctness, privacy preservation, and robustness. Experimental results and comparisons show the proposed scheme is satisfactory in terms of computation cost and communication overhead.

A secure and privacy-preserving data aggregation and classification model for smart grid

A secure and privacy-preserving data aggregation and classification model for smart grid

Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid

As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-based smart grid (FCSG) face serious challenges in security and privacy. In this paper, we propose a privacy-preserving data aggregation scheme that supports dynamic billing and arbitration, named PPDB. Specifically, we design a four-layer data aggregation framework which uses fog nodes (FNs) to collect and aggregate electricity consumption data encrypted under the ElGamal cryptosystem and employ distributed decryption to achieve fine-grained access and bills generation based on real-time prices. In addition, we introduce a trusted third party to arbitrate disputed bills. Detailed security analysis proves that the proposed PPDB can guarantee the confidentiality, authentication and integrity of data. Compared with related schemes, the experimental results show that the communication overhead of our scheme is reduced by at least 38%, and the computational efficiency in the billing phase is improved by at least 40 times.