Privacy-preserving Data Aggregation Scheme Research Articles

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

A Lightweight Lattice-Based Homomorphic Privacy-Preserving Data Aggregation Scheme for Smart Grid

Consumer privacy and consumption confidentiality and integrity are the main security concerns for smart grid connection with the residential electricity consumers. This paper proposes a lightweight privacy-preserving electricity consumption aggregation scheme that exploits lightweight lattice-based homomorphic cryptosystem. In the proposed scheme, smart household appliances aggregate their readings without involving the smart meter. Although smart meters or the intermediate base station cannot decrypt this aggregated consumption, they can validate the message’s authenticity. The proposed scheme also investigates the impact of different types of smart appliances on the home area network’s overhead. The total communication and computation load for the proposed scheme is trivial and tolerable by different parties in the connection, i.e., smart appliances, smart meters, and the base station. In addition, the deployed cryptosystem, which depends on simple arithmetic operations, can further reduce the computation duty for smart appliances. Simulation results and security analysis show that our proposed scheme guarantees consumers privacy, and messages authenticity and integrity, with lightweight communication and computation complexity.

A secure ECC-based privacy preserving data aggregation scheme for smart grids

Development of Smart Grid and deployment of smart meters in large scale has raised a lot of concerns regarding customers’ privacy. Consequently, several schemes have been proposed to overcome the above mentioned issue. These schemes mainly rely on data aggregation as a method of protecting users’ privacy from the grid operators. However, the main problem with most of these schemes is the fact that they require a large amount of processing power at the meter side. This, together with the fact that smart meters don’t usually have a powerful processor, can cause the unavailability of smart meter data at the required time for operators of the grid, and at the same time prevents smart meters from performing their other duties, such as managing the home area network. In this paper, we propose an efficient privacy preserving data aggregation scheme, based on elliptic curves. In our scheme, each smart meter signs its encrypted data and sends it to the aggregator. After receiving the data, the aggregator verifies the incoming messages and aggregates them, without the need to decrypt the data received from each smart meter. The aggregator signs the aggregated messages and sends it to the operation center. At the end, the smart meters’ data are obtained and verified by the operation center. In addition to providing efficient computation cost, for the users, our protocol also satisfies the security requirements of smart grid data aggregation schemes. We provide security analysis of the proposed scheme as well as comparisons to show the efficiency of our scheme on computation and communication overheads.

Achieving privacy-preserving big data aggregation with fault tolerance in smart grid

In a smart grid, a huge amount of data is collected for various applications, such as load monitoring and demand response. These data are used for analyzing the power state and formulating the optimal dispatching strategy. However, these big energy data in terms of volume, velocity and variety raise concern over consumers’ privacy. For instance, in order to optimize energy utilization and support demand response, numerous smart meters are installed at a consumer's home to collect energy consumption data at a fine granularity, but these fine-grained data may contain information on the appliances and thus the consumer's behaviors at home. In this paper, we propose a privacy-preserving data aggregation scheme based on secret sharing with fault tolerance in a smart grid, which ensures that the control center obtains the integrated data without compromising privacy. Meanwhile, we also consider fault tolerance and resistance to differential attack during the data aggregation. Finally, we perform a security analysis and performance evaluation of our scheme in comparison with the other similar schemes. The analysis shows that our scheme can meet the security requirement, and it also shows better performance than other popular methods.

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems.

Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles’ location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency.

A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices’ data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices’ data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.

A Secure Privacy-Preserving Data Aggregation Scheme Based on Bilinear ElGamal Cryptosystem for Remote Health Monitoring Systems

Due to advancements in the development of wireless medical sensing devices and wireless communication technologies, the wireless body area network (WBAN) has become an eminent part of e-healthcare systems. WBAN uses medical sensors to continuously monitor and collect the physiological parameters of a patient’s health and send them to a remote medical server through a portable digital assistance (PDA)/mobile. Due to limitations in communication, such as power, storage, and the computational capabilities of sensors, data aggregation techniques are used to reduce the communication overhead in real-time data transmission in WBAN. However, since the WBAN transmits sensitive health data, data security and data privacy are a major concern. In this paper, we propose a secure privacy-preserving data aggregation (SPPDA) scheme based on bilinear pairing for remote health monitoring systems to improve data aggregation efficiency and data privacy. Our proposed SPPDA scheme utilizes the homomorphic property of the bilinear ElGamal cryptosystem to perform privacy-preserving secure computation and combines it with the aggregate signature scheme, enabling data authenticity/integrity in the WBAN. The proposed SPPDA scheme is proved to be semantically secure under the decisional bilinear Diffie–Hellman assumption. Security analysis demonstrates that our proposed scheme preserves data confidentiality, data authenticity, and data privacy; it also resists passive eavesdropping and replay attacks. A performance evaluation based on simulation results and a comparison of computational cost with related schemes show that data aggregation and batch verification at the PDA significantly reduce communication and transmission overhead and support efficient computation at the remote server.

Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grid Against Internal Adversaries

Privacy-preserving data aggregation (P2DA) is an important basic building block that can protect consumer’s privacy in the smart grid environment because it could be used to prevent the extraction of the electricity consumption information of a specific consumer. Due to this important function, the P2DA scheme for the smart grid has attracted a lot of attention from both academic and industry researchers who have proposed many P2DA schemes for the smart grid in recent years. However, most of these P2DA schemes are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter’s side. To address the deficiencies of previous schemes, we propose a new P2DA scheme against internal attackers using Boneh–Goh–Nissim public key cryptography. The proposed P2DA scheme does not use bilinear pairing or hash-to-point operation making it be more computationally efficient than previous P2DA schemes. We also show that the proposed P2DA scheme is provably secure and can meet various security requirements.

Design Perspectives of People Centric Sensing Systems

Mobile devices play a vital role in our day to day life. The various sensing modalities available in mobile devices are identified and justify the growth of the capabilities on today’s smart phones.Taking advantage of the sensing capabilities of the mobile devices paves way for a new era of network, referred as people centric sensing network. In this paper, a broad study about the applications, architectural components and the existing privacy and security architectures available are discussed. The applications of mobile sensing are categorized as personal, social and public sensing based on what they sense, how they share and infer. All these applications clearly depict the roles played by people. People may act as end users, as participants and as application administrators. Since people are in the loop, the basic privacy and security requirements are analyzed that can be realized as the need in the design of any mobile phone sensing systems. The possible threats that arise in this sensing context may be due to both internal and external entities. The strategies and types of adversarial models in the people centric sensing approach provides us light to how the framework works and solutions needed. Existing literature in terms of privacy preserving data aggregation schemes and security Frameworks are also discussed in detail. Applications: The model clearly portraits the importance of people who are no more only passive data users. More focus has to be given on privacy preserving models to make the system more acceptable by the people.

A survey on Privacy Preserving Data Aggregation Schemes in People Centric Sensing Systems and Wireless Domains

Background: The technological advances in mobile and wireless communication have paved way for a new era of large scale sensing network. Smart phones used by people are nowadays equipped with rich set of sensing capabilities. These mobile devices carried by people can be used together to form a network called people centric sensing network. Methods: Since people are the mobile custodians, privacy becomes a threat to the network. This paper focuses on a study about the various privacy preserving aggregation schemes existing in other domains of the wireless network including people centric sensing. The design goals and challenges that arise when attempting to protect the user’s sensitive data are analyzed. The techniques commonly used to achieve user’s privacy are the homomorphic property of encryption schemes, Data slicing and Mix schemes. Findings: A study about the existing privacy preserving aggregation schemes enables us to design a new method that is more suitable for people centric sensing network. The design considerations involved in developing a security and privacy framework are also identified. The privacy preserving aggregation scheme should support spatial and temporal aggregation with fault tolerance. The scheme has to be adaptable for both additive and non-additive statistical aggregation functions. Also maintaining the data integrity, data privacy and data accuracy with less computational and communication overhead is essential. Applications: Privacy protection is important for user’s participation which supports large set of cooperative sensing applications. Irrespective of various domains in wireless networks, the privacy preserving data aggregation model is discussed that is suitable for the context of the applications.

A Privacy-Preserving Data Aggregation Scheme in Wireless Sensor Networks

A Privacy-Preserving Data Aggregation Scheme in Wireless Sensor Networks

A Privacy-Preserving Health Data Aggregation Scheme

Patients’ health data is very sensitive and the access to individual’s health data should be strictly restricted. However, many data consumers may need to use the aggregated health data. For example, the insurance companies needs to use this data to setup the premium level for health insurances. Therefore, privacy-preserving data aggregation solutions for health data have both theoretical importance and application potentials. In this paper, we propose a privacy-preserving health data aggregation scheme using differential privacy. In our scheme, patients’ health data are aggregated by the local healthcare center before it is used by data comsumers, and this prevents individual’s data from being leaked. Moreover, compared with the existing schemes in the literature, our work enjoys two additional benefits: 1) it not only resists many well known attacks in the open wireless networks, but also achieves the resilience against the human-factor-aware differential aggregation attack; 2) no trusted third party is employed in our proposed scheme, hence it achieves the robustness property and it does not suffer the single point failure problem.

Privacy-preserving data aggregation scheme against internal attackers in smart grids

With fast advancements of communication, systems and information technologies, a smart grid (SG) could bring much convenience to users because it could provide a reliable and efficient energy service. The data aggregation (DA) scheme for the SG plays an important role in evaluating information about current energy usage. To achieve the goal of preserving users' privacy, many DA schemes for the SG have been proposed in last decade. However, how to withstand attacks of internal adversaries is not considered in those schemes. To enhance preservation of privacy, Fan et al. proposed a DA scheme for the SG against internal adversaries. In Fan et al.'s DA scheme, blinding factors are used in evaluating information about current energy usage and the aggregator cannot get the consumption information of any individual user. Fan et al. demonstrated that their scheme was secure against various attacks. However, we find that their scheme suffers from the key leakage problem, i.e., the adversary could extract the user's private key through the public information. To overcome such serious weakness, this paper proposes an efficient and privacy-preserving DA scheme for the SG against internal attacks. Analysis shows that the proposed DA scheme not only overcome the key leakage problem in Fan et al.'s DA scheme, but also has better performance.

An Efficient and Verifiable Privacy-Preserving Data Aggregation Scheme for Wireless Sensor Networks

Data aggregation is an important techniques to save energy consumption in wireless sensor networks. However, the compromised aggregators may forge fake values as the aggregated results of their child nodes. In this paper, we proposed an efficient secure data aggregation scheme to overcome the problem. Integrated with the privacy homomorphism and the aggregate message authentication code techniques, our proposal can provide both end-to-end privacy and data integrity in data aggregation for sensor networks. The performance analysis shows that compared with other existing secure data aggregation schemes, the proposed scheme has remarkable advantages in terms of computation and communication overhead.

PDAFT: A privacy-preserving data aggregation scheme with fault tolerance for smart grid communications

Smart grid, as the next generation of power grid featured with efficient, reliable, and flexible characteristics, has received considerable attention in recent years. However, the full flourish of smart grid is still hindered by how to efficiently and effectively tackle with its security and privacy challenges. In this paper, we propose a privacy-preserving data aggregation scheme with fault tolerance, named PDAFT, for secure smart grid communications. Specifically, PDAFT uses the homomorphic Paillier Encryption technique to encrypt sensitive user data such that the control center can obtain the aggregated data without knowing individual ones, and a strong adversary who aims to threaten user privacy can learn nothing even though he has already compromised a few servers at the control center. In addition, PDAFT also supports the fault-tolerant feature, i.e., PDAFT can still work well even when some user failures and server malfunctions occur. Through extensive analysis, we demonstrate that PDAFT not only resists various security threats and preserves user privacy, but also has significantly less communication overhead compared with those previously reported competitive approaches.

A Signature-Based Data Security Technique for Energy-Efficient Data Aggregation in Wireless Sensor Networks

Data aggregation techniques have been widely used in wireless sensor networks (WSNs) to solve the energy constraint problems of sensor nodes. They can conserve the significant amount of energy by reducing data packet transmission costs. However, many data aggregation applications require privacy and integrity protection of the real data while transmitting data from the sensing nodes to a sink node. The existing schemes for supporting both privacy and integrity, that is, iCDPA, and iPDA, suffer from high communication cost, high computation cost, and data propagation delay. To resolve the problems, we propose a signature-based data security technique for protecting sensitive data aggregation in WSNs. To support privacy-preserving data aggregation and integrity checking, our technique makes use of the additive property of complex numbers. Out of two parts of a complex number, the real part is used to hide the sampled data of a sensor node from its neighboring nodes and adversaries, whereas the imaginary part is used for data integrity checking at both data aggregators and the sink node. Through a performance analysis, we prove that our privacy-preserving data aggregation scheme outperforms the existing schemes up to 50% in terms of communication and computation overheads as well as up to 3 times in terms of integrity checking and data propagation delay.

PDA: A Novel Privacy-Preserving Robust Data Aggregation Scheme in People-Centric Sensing System

With the advancement of wireless communication technologies, mobile phones, PDAs, and car embedded devices are equipped with sensors, such as sound and image. People can apply these devices to form a new sensing network called people-centric sensing network. And this network offers new opportunities for cooperative sensing applications. However, it introduces some challenges, including security challenge and robust challenge. As sensor nodes need to send their individual sensed data to an aggregator node and these data are related to users' real life, privacy-preserving data aggregation is a challenge issue. As a node could become offline or a message could be lost before reaching the aggregator, retaining the correctness of the aggregate computed is important. In this paper, we present the design of PDA, a novel privacy-preserving robust data aggregation scheme in people-centric sensing system. Based on K-anonymity, homomorphic encryption, and secret sharing, PDA can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Subtraction, Average, Count, Max/Min, and Median without leaking individual sensed data. Moreover, PDA is robust to node failure and data loss. We also evaluate the efficacy and efficiency of PDA. The result shows that our scheme can achieve the security and robust goal under a reasonable cost.

SP2DAS: Self-certified PKC-Based Privacy-Preserving Data Aggregation Scheme in Smart Grid

Smart grid is a network of computers and power infrastructures that monitor and control energy usage by collecting data from the power grid. It can gather and distribute information about the behavior of all consumers in order to improve the efficiency, reliability, economics, safety, and sustainability of electricity services. In this paper, we propose a self-certified PKC-based privacy-preserving data aggregation scheme in smart grid to increase computation efficiency and achieve privacy protection of end users. To realize the anonymous aggregation of multidimensional data, we adopt the Chinese Remainder Theorem and homomorphic property of Paillier cryptosystem to achieve it. Comparing our scheme with Lu et al.'s scheme, the result shows that our scheme has more advantages over Lu et al.'s scheme in terms of computational costs of the user, GW, and OA. After adopting batch verification technique, the computational cost of GW is constant in our scheme, however, that of GW is linear with the number of the users in Lu et al.'s scheme. Furthermore, our scheme also supports the anonymity of the user's identity. It indicates that the local gateway GW does not know the real identity of the resident user such that the privacy of the user is better protected.

An Efficient Privacy Preserving Data Aggregation Scheme with Constant Communication Overheads for Wireless Sensor Networks

Providing efficient data aggregation while preserving data privacy in wireless sensor networks (WSNs) is a challenging problem. Existing security schemes either incur high communication and computational overheads or simply fail to counter attacks when nodes are compromised. In this paper, we present a multidimensional privacy preserving data aggregation scheme for WSNs which is efficient and provides strong security. The scheme not only provides efficient countermeasure against passive and active privacy compromising attacks, coalition attacks from malicious base station and captured sensor nodes, but also is robust to data loss. In addition, the proposed scheme provides data aggregation with constant communication overheads, so that the transmission cost can be significantly reduced which makes it suitable to be used in large scale WSNs. To the best of our knowledge, our scheme is the first one that addresses the privacy and efficiency issues in WSNs all at once.

Preserving privacy in wireless sensor networks using reliable data aggregation

An important topic addressed by the wireless sensor networks community over the last several years is the in-network data aggregation. It is significant as well as a challenging issue to provide reliable data aggregation scheme while preserving data privacy. However, in WSNs, achieving ideal data accuracy is complicated due to collision, heavy network traffic, processing delays and/or several attacks. The problem of gathering accurate integrated data will be further intensified if the environment is adverse. Hence how to attain data privacy and perfect data accuracy are two major challenges for data aggregation in wireless sensor networks. To address this problem, we propose in this paper a new privacy preserving data aggregation scheme. We present REBIVE (REliaBle prIVate data aggrEgation scheme). In REBIVE the data accuracy maintenance and data privacy protection mechanisms work cooperatively. Different from past research, our proposed solution have the following features: providing privacy preservation technique for individual sensor data and aggregated sensor data; maintaining perfect data accuracy for realistic environments; being highly efficient; and being robust to popular attacks launched in WSNs.