Privacy-preserving Data Aggregation Scheme Research Articles

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.

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

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.

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.

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

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.

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.

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.

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.

OPERA: Optional Dimensional Privacy-Preserving Data Aggregation for Smart Healthcare Systems

Massive multidimensional health data collected from Internet of Things (IoT) devices are driving a new era of smart health, and with it come privacy concerns. Privacy-preserving data aggregation (PDA) is a proven solution providing statistics while hiding raw data. However, existing PDA schemes ignore the willingness of data owners to share, so data owners may refuse to share data. To increase their willingness to contribute data, we propose an OPtional dimEnsional pRivacy-preserving data Aggregation scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(OPERA)</i> to provide data contributors with options on sharing dimensions while keeping their choices and data private. OPERA uses selection vectors to represent the decisions of users and count participants dimensionally and achieves data privacy and utility based on a multisecret sharing method and symmetric homomorphic cryptography. Analyses show that in OPERA, the probability of adversaries breaching privacy is less than 4.68e-97. Performance evaluations demonstrate that OPERA is outstanding in computation and practical in communication.

Blockchain-Enabled Secure and Privacy-Preserving Data Aggregation for Fog-Based ITS

As an essential component of intelligent transportation systems (ITS), electric vehicles (EVs) can store massive amounts of electric power in their batteries and send power back to a charging station (CS) at peak hours to balance the power supply and generate profits. However, when the system collects the corresponding power data, several severe security and privacy issues are encountered. The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids. Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things (IoT) environment. To address above problems, this paper proposes a blockchain-enabled secure and privacy-preserving data aggregation scheme for fog-based ITS. First, a fog computing and blockchain co-aware aggregation framework of power injection data is designed, which provides strong support for ITS to achieve secure and efficient power injection. Second, Paillier homomorphic encryption, the batch aggregation signature mechanism and a Bloom filter are effectively integrated with efficient aggregation of power injection data with security and privacy guarantees. In addition, the fine-grained homomorphic aggregation is designed for power injection data generated by all EVs, which provides solid data support for accurate power dispatching and supply management in ITS. Experiments show that the total computational cost is significantly reduced in the proposed scheme while providing security and privacy guarantees. The proposed scheme is more suitable for ITS with latency-sensitive applications and is also adapted to deploying devices with limited resources.

Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

The Industrial Internet of Things (IIoT) is the key technology of Industry 4.0. The combination of machine learning and IIoT has spawned a thriving smart industry. Machine learning models are trained and predicted based on raw data that contains sensitive information, and data sharing leads to information leakage. Data security and privacy protection in IIoT face serious challenges. Therefore, we propose a federated learning-based privacy-preserving data aggregation scheme (FLPDA) for IIoT. Data aggregation to protect individual user model changes in federated learning against reverse analysis attacks from industry administration centers. Each round of data aggregation uses the PBFT consensus algorithm to select an IIoT device from the aggregation area as the initialization and aggregation node. Paillier cryptosystem and secret sharing are combined to realize data fault tolerance and secure sharing. Security analysis and performance evaluation show that the scheme can effectively protect data privacy and resist various attacks. It has lower communication, computational, and storage overhead than existing schemes.

Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT

The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.

Efficient Function Queryable and Privacy Preserving Data Aggregation Scheme in Smart Grid

The collection of users' near-real-time electricity consumption data brings advantages to the operation of smart grids, while raising some security and privacy issues. Multiple privacy preserving data aggregation schemes have been proposed to address these problems. However, most schemes only focus on the aggregation of electricity consumption data without considering the data availability. In addition, although a data aggregation scheme that supports function queries on encrypted data has also been developed, its efficiency is insufficient. In this paper, we first propose an EC-ElGamal encryption algorithm with a double trapdoor decryption mechanism. Through employing the proposed algorithm and the elliptic curve Schnorr signature scheme, an efficient data aggregation scheme supporting privacy protection and function query is proposed for smart grids. This solution allows the control center and users to initiate various function queries on encrypted data. In order to lighten the calculation burden of the control center, we propose another cryptosystem named ElGamal-OU to improve the decryption efficiency, which also supports two independent decryption methods. Finally, the security analysis and performance comparison with related work show that our schemes have advantages in terms of computational, communication and storage overhead.

An Efficient and Robust Multidimensional Data Aggregation Scheme for Smart Grid Based on Blockchain

In order to analyze real-time power data without revealing users’ privacy, privacy-preserving data aggregation schemes have been extensively researched in smart grid. However, most of the existing schemes either can only allow stationary users, or require a trusted center. In this paper, we propose an efficient and robust multidimensional data aggregation scheme based on blockchain. In our scheme, a leader election algorithm in Raft protocol is used to select a mining node from all smart meters to aggregate data. A dynamically verifiable secret sharing homomorphism scheme is adopted to realize flexible dynamic user management. In addition, our scheme can not only resist internal and external attacks but also support multidimensional data aggregation and fault tolerance. The security analysis shows that our proposed scheme is IND-CPA secure and can meet stronger security features. The experimental results show that compared with other schemes, our scheme can be implemented with lower computation and communication overhead.

Privacy-Preserving and Customization-Supported Data Aggregation in Mobile Crowdsensing

Data aggregation is a fundamental problem in mobile crowdsensing (MCS). However, the existing approaches are still unsatisfactory considering the privacy protection of sensing data and aggregation results. In addition, most existing privacy-preserving data aggregation schemes can only support a single type of aggregation, which limits their application scenarios. To address these issues, we propose a novel privacy-preserving and customization-supported data aggregation scheme that can achieve multiple types of aggregation. Specifically, we utilize additive secret sharing ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> ) to protect the privacy of both sensing data and aggregation results and then propose a simplified secure triplet generation protocol based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> to construct secure aggregation operations. Moreover, we design a secure comparison (SC) algorithm and a secure top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> algorithm to realize customized aggregation (i.e., statistical aggregation over top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> largest or smallest values of sensing data). The formal theoretical analysis demonstrates that the proposed scheme is effective, and the extensive experiments conducted on a real-world data set show that the proposed approach is privacy preserving and efficient.

An efficient and privacy-preserving data aggregation scheme supporting arbitrary statistical functions in IoT

The Internet of Things (IoT) has profoundly impacted our lives and has greatly revolutionized our lifestyle. The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions. However, the high frequency of collecting user data will raise people concerns about personal privacy. In recent years, many privacy-preserving data aggregation schemes have been proposed. Unfortunately, most existing schemes cannot support either arbitrary aggregation functions, or dynamic user group management, or fault tolerance. In this paper, we propose an efficient and privacy-preserving data aggregation scheme. In the scheme, we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence. On this basis, the proposed scheme supports not only arbitrary aggregation functions, but also flexible dynamic user management. Furthermore, the scheme achieves fault-tolerant capabilities by utilizing a future data buffering mechanism. Security analysis reveals that the scheme can achieve the desired security properties, and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

Lightweight and Fine-Grained Privacy-Preserving Data Aggregation Scheme in Edge Computing

Edge computing is a new computing paradigm, whichprovides computing resources at the edge of the network close to the data source and reduces the response delay and data transmission bandwidth. Data aggregation is a fundamental computing service in edge computing. However, existing data aggregation schemes do not consider the problem of fine-grained data aggregation. Besides, most schemes use a pair-based signature scheme, which cannot meet the actual situation of limited resources of terminal devices. In this article, we propose a lightweight, fine-grained privacy-preserving data aggregation scheme. The terminal devices add the user’s characteristic identifier bit in the generated message. Therefore, the edge servers can combine the characteristic identifiers in the messages to perform fine-grained data aggregation according to the aggregation rule list to obtain aggregated data of multiple target groups. We also use the unpaired signature scheme based on the elliptic curve, which has a faster signature and verification speed. Also, we use masks to avoid the privacy leakage of user’s characteristic identifiers. According to the experiments, the computation and communication overhead of our scheme is low. Simultaneously, the cloud center can dynamically update the aggregation rule list sent to the edge server according to the demands.