Preserving Data Aggregation Scheme Research Articles

Privacy and integrity-preserving data aggregation scheme for wireless sensor networks digital twins

The security technology of digital twin is an important guarantee to ensure the security of digital twin operation, which mainly includes network security technology, data security technology and privacy protection technology. In wireless sensor networks, data aggregation technologies are known as a suitable solution to reduce energy consumption. In addition, due to wireless communications, wireless sensor networks are subject to many attacks. Therefore, it is very important to provide data security in the data aggregation process. In this paper, in order to protect data privacy and verify data integrity, moreover, balance the energy consumption and security during the data aggregation, we present a privacy and integrity–preserving data aggregation scheme for wireless sensor networks based on digital twins technology and homomorphic fingerprinting (HFPIDA). The HFPIDA adopts privacy function to protect data privacy and adopts homomorphic fingerprinting technology to verify the aggregation data integrity. Security analysis shows that the HFPIDA can effectively preserve data privacy and verify data integrity. Simulation results show that the HFPIDA requires less communication and energy overheads, and can achieve higher aggregation accuracy.

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.

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.

A distributed privacy-preserving data aggregation scheme for smart grid with fine-grained access control

Smart grid integrates smart meters and modern communication technologies with the traditional electricity grid. It not only improves efficiency in power resource management, but also enhances reliability of the grid. However, the power consumption data may reveal users’ privacy, e.g. their living habits, and its authenticity is also important for accurate billing. Although a number of privacy preserving data aggregation schemes for smart grid have been proposed in the literature, most of them still suffer some limitations. For example, a single party is normally required to decrypt the aggregated data and this party could be a single point of failure in the system. Moreover, once the aggregated data is decrypted, no access control is imposed and it is available to the public. In order to address the above problems, we introduce a distributed privacy-preserving data aggregation scheme for smart grid with fine-grained access control. In the proposed scheme, homomorphic encryption is employed to ensure user privacy, digital signature is used to ensure data authenticity, distributed proxy re-encryption is used to achieve fine-grained access control of the aggregated data, and zero-knowledge proof is used to ensure that the proxy servers perform their tasks properly. Security analyses prove that the proposed scheme achieves the desirable security properties. In particular, it does not suffer single point of failure and the aggregated data can only be accessed by the designated recipient. Efficiency analyses also demonstrate that it is practical for real-world applications.

A novel privacy preserving data aggregation scheme with data integrity and fault tolerance for smart grid communications

A novel privacy preserving data aggregation scheme with data integrity and fault tolerance for smart grid communications

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.

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.

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.

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 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.

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 survey on the privacy-preserving data aggregation in wireless sensor networks

Wireless sensor networks (WSNs) consist of a great deal of sensor nodes with limited power, computation, storage, sensing and communication capabilities. Data aggregation is a very important technique, which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a WSNs. However, privacy-preservation is more challenging especially in data aggregation, where the aggregators need to perform some aggregation operations on sensing data it received. We present a state-of-the art survey of privacy-preserving data aggregation in WSNs. At first, we classify the existing privacy- preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each scheme. And then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability, communication consumption, power consumption and data accuracy etc. Furthermore, based on the existing work, we also discuss a number of open issues which may intrigue the interest of researchers for future work.

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.