Privacy-preserving Data Aggregation Scheme Research Articles

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

LVPDA: A Lightweight and Verifiable Privacy-Preserving Data Aggregation Scheme for Edge-Enabled IoT

Edge computing is envisioned to be a powerful platform that provides efficient data storage and computation services in the smart Internet-of-Things (IoT) systems. In this data-intensive architecture, protecting user-side data privacy is one of the most critical concerns to prevent privacy leakage from any other untrusted entities. Aiming to resist this concern, many privacy-preserving data aggregation (PPDA) schemes have been proposed for various cloud-enabled IoT applications. However, due to the resource-constrained nature of the smart IoT devices, the conventional PPDA solutions, in terms of both privacy and performance requirements, are unsuitable in edge computing. To address this challenge, we propose a lightweight and verifiable PPDA scheme, named LVPDA, for the edge-computing-enabled IoT system, where the Paillier homomorphic encryption method and an online/offline signature technique are combined to ensure the privacy preserving and integrity verification during the data aggregation process. A detailed security analysis indicates that LVPDA is existentially unforgeable under the chosen message attack (EU-CMA) and the data integrity can be guaranteed with formal proof under $q$ -strong Diffie–Hellman ( $q$ -SDH) assumptions. Compared with other PPDA methods, our scheme can achieve lightweight PPDA in terms of less computational complexity and communication overhead.

Aggregate in my way: Privacy-preserving data aggregation without trusted authority in ICN

Information-Centric Networking (ICN) is a novel future network architecture which in contrast to IP-based networks relies on content and its name. It separates the physical location of data from the discovery and forwarding process and solely relies on the content itself. For the Internet of Thing (IoT) networks, stripping the location information may provide privacy, but this does not translate to operational privacy. Attackers can infer user behavior patterns through operational privacy, by eavesdropping on meaningful information. The content name and designed signature in ICN can associate content with the identity of the provider, even if the IP address is hidden. Although several research efforts focus on the privacy protection of ICN, they do not consider the privacy implications of data aggregation. Most existing privacy-preserving data aggregation protocols are designed for special operations, such as sum, average, variance, max or min, and they cannot support arbitrary aggregation operations in the ciphertexts domain. Besides, the need for trusted authority (TA) restricts the use of existing protocols in the real world. In this paper, we propose a practical and privacy-preserving data aggregation scheme that can compute arbitrary aggregation functions without a TA. On one hand, our scheme can ensure users’ anonymity and privacy protection, while on the other, the scheme is efficient in enabling participants to join or leave the system dynamically. Security analysis shows that the proposed scheme can achieve the desired security properties, while experimental results demonstrate its effectiveness and efficiency.

An efficient aggregation scheme resisting on malicious data mining attacks for smart grid

In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature. In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary can infer a target user’s electricity usage data. When considering this attack, the majority of existing data aggregation schemes have one of the following two shortcomings. In one aspect, the schemes based on homomorphic encryption can output an accurate aggregation result, but most of them are vulnerable to this attack. In another aspect, the schemes based on differential privacy able to withstand this attack, but the random noises introduced prevent accurate aggregation results from being computed. In this paper, we propose a novel data aggregation scheme that is not only secure against the malicious data mining attack, but also capable of outputting an accurate aggregation result. Detailed security analyses indicate that the proposed scheme satisfies the desirable properties for privacy-preserving data aggregation in the smart grid, and the simulated results demonstrate that our proposed scheme enjoys low computation and communication overhead.

A secure and lightweight privacy-preserving data aggregation scheme for internet of vehicles

Data aggregation can effectively improve resource utilization in Internet of Vehicles (IoV), and has attracted broad attention recently. However, because of the particularity of nodes in IoV, data aggregation faces security challenges such as location privacy, data privacy and accuracy of aggregation results. To solve these challenges, in this paper, we propose a secure and lightweight privacy-preserving data aggregation scheme, named SLPDA, for IoV. Specifically, SLPDA employs Chinese Remainder Theorem to map multi-dimensional data to one-dimensional data, and uses masking technology to encrypt data, thus roadside units can realize lightweight data aggregation without plaintext. SLPDA also adopts Identity-based batch authentication technology to reduce authentication overhead. Detailed security analysis indicates that SLPDA can resist various security threats. In addition, the performance evaluations demonstrate that SLPDA is more efficient than the reported schemes in terms of computation complexity and communication overhead.

A privacy-preserving data aggregation scheme for dynamic groups in fog computing

Fog computing has garnered significant attention in recent years, since it can bridge the cloud and terminal devices and provide low latency, location awareness, and geo-distribution at the edge of the network. Data aggregation is a prime candidate for fog computing applications. However, most previous works about data aggregation do not focus on the fog computing. In addition, existing secure data aggregation schemes in fog computing usually do not support dynamic groups and arbitrary aggregation functions. In this paper, we construct concrete data encryption, data aggregation and data decryption algorithms, and further propose a privacy-preserving and collusion-resistant data aggregation scheme for dynamic groups in fog computing. Specifically, in the proposed protocol, the cloud server can periodically collect raw data and compute arbitrary aggregation functions on them. Even if some malicious terminal devices collude with the fog device or the cloud server, the honest terminal devices’ privacy cannot be breached. The fog device can filter out false data and aggregate all terminal devices’ ciphertexts to save the bandwidth. Besides, dynamic join and exit of terminal devices is achieved. Detailed security analysis shows that our scheme holds k-source anonymity. Our scheme is also demonstrated to be efficient via extensive experiments.

A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users’ privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.

A secure and energy-efficient platform for the integration of Wireless Sensor Networks and Mobile Cloud Computing

Wireless Sensor Networks suffer from power and energy consumption issues because the batteries used are small and power-limited. Mobile Cloud Computing suffers from several security threats such as integrity and privacy issues. The integration of both areas results in better energy conservation and a more secure environment. Thus, this research introduces a secure energy-efficient platform that reduces the energy consumption and maintains privacy. The security approach of the platform utilizes a modified version of the Sharing-based Scheme and a Precision-enhanced and Encryption-mixed Privacy-preserving Data Aggregation scheme. The first supports both authentication and encryption using XOR gates, while the second is a slicing, secure data aggregation protocol that enhances both security and energy. For energy consumption reduction, asynchronous scheduling duty cycling based on Location, Priority and Pre-Configuration is introduced. The results show that the platform depends on the rate of sensing, frequency of sending data, data size, location and number of sleeping sensors, and smartphone battery capacity. In the case of less frequent rates and lower data sizes, the operational energy consumption is 1% of the mobile's entire battery capacity. In the case of sensors sleeping next to the sink, the cost is reduced by over 70% with an additional cost of 20% over the un-secured network. The simulations show that the encryption cost decreases as the number of sensors increases. Furthermore, when the number of sensors increase, the distance between the node decreases and therefore more sensors are tending to sleep, which leads to less energy consumption. As a result, the platform introduced in this work outperforms existing schemes for large numbers of sensors (greater than 300 sensors) with an additional average security cost of 2.96%.

A lightweight privacy-preserving data aggregation scheme with provable security for internet-of-things

In this work, an autonomous lightweight certificateless aggregate signature-based data aggregation scheme for internet-of-things (IoT) is proposed. In the scheme, IoT terminals only need to contact the key generation center (KGC) once. Subsequently, they can generate their pseudonyms on their own without any interaction with the KGC. An efficient revocation mechanism is provided to punish malicious terminals. The proposed scheme is anonymous, can provide conditional traceability, resist various attacks, and solves private key compromise problem. The formal security proof demonstrates that the scheme is secure against both Type I and Type II adversaries in the random oracle model based on the intractability of the Discrete Logarithm Problem. The performance evaluation shows that the proposed scheme is more efficient than the state-of-the-art schemes and fits the resource-constraint IoT environment.

A Privacy-Aware Data Aggregation Scheme for Smart Grid Based on Elliptic Curve Cryptography With Provable Security Against Internal Attacks

In smart grids (SGs), smart meters (SMs) are usually deployed to collect and transmit customers' electricity consumption data in real-time to the control center. Due to the open nature of the SG communication, several privacy-preserving data aggregation schemes have been proposed to protect the privacy of customers. However, most of these schemes cannot protect against internal attackers and they are not efficient, since SMs are constrained in processing, memory, and computing capabilities. To address these problems, the authors propose a privacy-aware lightweight data aggregation scheme against internal attackers based on Elliptic Curve Cryptography (ECC). The scheme satisfies all the security requirements of SG, and supports conditional traceability, strong anonymity and autonomy. The authors demonstrate that the proposed scheme provides confidentiality based on the Computational Diffie-Hellman (CDH) assumption and unforgeability in the security model based on the intractability of the Discrete Logarithm (DL) problem. Extensive performance analysis shows that the proposed scheme is very efficient.

Privacy Preserving Data Aggregation Scheme for Mobile Edge Computing Assisted IoT Applications

As the rapid development of 5G and Internet of Things (IoT) techniques, more and more mobile devices with specific sensing capabilities access to the network and large amounts of data. The traditional architecture of the cloud computing cannot satisfy the requirements, such as low latency, fast data access for IoT applications. Mobile edge computing (MEC) can solve these problems, and improve the execution efficiency of the system. In this paper, we propose a privacy preserving data aggregation scheme for MEC assisted IoT applications. In our model, there are three participants, i.e., terminal device (TD), edge server (ES), and public cloud center (PCC). The data generated by the TDs is encrypted and transmitted to the ES, then the ES aggregates the data of the TDs and submits the aggregated data to the PCC. At last, the aggregated plaintext data can be recovered by PCC through its private key. Our scheme not only guarantees data privacy of the TDs but also provides source authentication and integrity. Compared with traditional model, our scheme can save half of communication cost, and is very suitable for MEC assisted IoT applications.

A Dynamic Membership Data Aggregation (DMDA) Protocol for Smart Grid

In order to protect the privacy of individual data, meantime guaranteeing the utility of big data, the privacy preserving data aggregation is widely researched, which is a feasible solution since it not only preserves the statistical feature of the original data, but also masks single user's data. With smart meter owning the capability of connecting to Internet, the aggregation area extends to the virtual area rather than a traditional physical area. However, in a virtual aggregation area, the users’ membership maybe frequently changes, if while executing the aggregation protocol for the traditional area, the overhead is not ignorable. In this paper, the homomorphic encryption and ID-based signature are employed to design a dynamic membership data aggregation (DMDA) scheme, which reduces the complexity on a new user's joining and an old user's quitting. In addition, the operation center obtains the sum of the data in the virtual aggregation area, meantime knows nothing about single user's data. Comparing with traditional privacy-preserving data aggregation scheme, DMDA is more suitable for next-generation smart grid and other Internet of Things environments.

A Practical Privacy-Preserving Data Aggregation (3PDA) Scheme for Smart Grid

The real-time electricity consumption data can be used in value-added service such as big data analysis, meanwhile the single user's privacy needs to be protected. How to balance the data utility and the privacy preservation is a vital issue, where the privacy-preserving data aggregation could be a feasible solution. Most of the existing data aggregation schemes rely on a trusted third party (TTP). However, this assumption will have negative impact on reliability, because the system can be easily knocked down by the denial of service attack. In this paper, a practical privacy-preserving data aggregation scheme is proposed without TTP, in which the users with some extent trust construct a virtual aggregation area to mask the single user's data, and meanwhile, the aggregation result almost has no effect for the data utility in large scale applications. The computation cost and communication overhead are reduced in order to promote the practicability. Moreover, the security analysis and the performance evaluation show that the proposed scheme is robust and efficient.

EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid

Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy utilization. However, security issues in communications still present practical concerns. To cope with these challenges, we propose EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid. Specifically, in the proposed scheme, we achieve both data source authentication and data aggregation in high efficiency. Besides, in order to adapt to the dynamic smart grid system, the threshold for aggregation is adjusted according to the energy consumption information of each particular residential area and the time period, which can support fault-tolerance while ensuring individual data privacy during aggregation. Detailed security analysis shows that our scheme can satisfy the desired security requirements of smart grid. In addition, we compare our scheme with existing schemes to demonstrate the effectiveness of our proposed scheme in terms of low computational complexity and communication overhead.

EPDAS: Efficient privacy-preserving data analysis scheme for smart grid network

Recently, several privacy-preserving data aggregation schemes have been proposed in smart grid system to achieve secure communication and protect consumer’s privacy. However, most of them are either inefficient, insecure or not fault-tolerant. To address these challenges, we propose a lightweight fault-tolerant privacy-preserving data aggregation scheme known as EPDAS using the combination of elliptic curve cryptography, modified Paillier cryptosystem, Chinese remainder theorem, and hash chain technique. Security analysis shows that EPDAS is secure and can provide confidentiality, anonymous authentication, autonomy, integrity, and fault tolerance. Extensive performance analysis demonstrates that the proposed scheme is more efficient than most recent related schemes. Therefore, EPDAS is suitable for practical implementation in smart grid environment.

APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT

Fog computing is a modern computing platform that connects the cloud with the edge smart devices located at the edge of the network. The fog computing platform has several characteristics desirable for Internet of Things (IoT) systems, such as the efficient data access, low latency, and location awareness. Data aggregation is a common operation in IoT systems. However, for data aggregation applications in the fog-enhanced IoT environment, how to efficiently preserve the privacy of sensitive data is a major concern. To address this challenge, we propose APPA: a device-oriented Anonymous Privacy-Preserving scheme with Authentication for data aggregation applications in fog-enhanced IoT systems, which also supports multi-authority to manage smart devices and fog nodes locally. In APPA scheme, the anonymity and authenticity of the device is guaranteed with pseudonym and pseudonym certificate, which can be updated autonomously. Taking the advantage of a local certification authority, the pseudonym management can be shifted to specialized fogs at the network edge, which provide real-time service for device registration and update. The data privacy can be ensured during data aggregation by using the Paillier algorithm. Detailed security analysis is conducted to show that our scheme can achieve security and privacy-preservation properties in the fog-enhanced IoT systems. Additionally, we compare our scheme with existing schemes to demonstrate the effectiveness and efficiency of our proposed scheme in terms of low computational complexity and communication overhead.

Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing.

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Efficient and Privacy-Preserving Data Aggregation and Dynamic Billing in Smart Grid Metering Networks

The smart grid enables convenient data collection between smart meters and operation centers via data concentrators. However, it presents security and privacy issues for the customer. For instance, a malicious data concentrator cannot only use consumption data for malicious purposes but also can reveal life patterns of the customers. Recently, several methods in different groups (e.g., secure data aggregation, etc.) have been proposed to collect the consumption usage in a privacy-preserving manner. Nevertheless, most of the schemes either introduce computational complexities in data aggregation or fail to support privacy-preserving billing against the internal adversaries (e.g., malicious data concentrators). In this paper, we propose an efficient and privacy-preserving data aggregation scheme that supports dynamic billing and provides security against internal adversaries in the smart grid. The proposed scheme actively includes the customer in the registration process, leading to end-to-end secure data aggregation, together with accurate and dynamic billing offering privacy protection. Compared with the related work, the scheme provides a balanced trade-off between security and efficacy (i.e., low communication and computation overhead while providing robust security).

Lightweight privacy-preserving data aggregation scheme for smart grid metering infrastructure protection

The electric industry's planned shift to smart grid metering infrastructure raised several concerns especially about preserving the privacy. Various data perturbation and aggregation solutions are being developed to address these concerns. The drawback of these solutions is that a simple random noise scheme cannot protect privacy, and there is a need for more advanced perturbation techniques to increase hardware costs of smart metering devices. The proposed data aggregation scheme combines the power of perturbation techniques with crypto-systems in an efficient and lightweight way so that it becomes applicable for devices with limited hardware, such as smart meters. We investigated the privacy preserving capabilities of the proposed aggregation scheme with Holt-Winters and Seasonal Trend Decomposition using Loess prediction methods. The results indicate that the proposed scheme is resilient to both filtering and true value attacks.

A Privacy Preserving Data Aggregation Scheme to Investigate Apps Installment in Massive Mobile Devices

Currently, mobile devices often try obtaining the list of installed applications to survey the install ratio for advertisers and app owners to evaluate the market share of their applications. However, adversaries could yet infer or even directly learn the user’s interests and personal information from his/her mobile application installations. Furthermore, this investigation, which could lead to user privacy leakage, now calls for more attention than ever before. Motivated to resolve the above issue, this paper presents a scheme to investigate the install ratio of mobile applications for app owners and advertisers while at the same time preserving the users’ privacy. The scheme is executed with one round communication between a server and mobile devices. According to experiment results, the proposed scheme can balance privacy protection and publishing the application install ratio accurately. Furthermore, the proposed scheme also satisfies differential privacy. This paper has also given the curve fitting of the amount of mobile devices and a privacy budget. It may instruct users how to choose a privacy budget for a given amount of devices to investigate.