Efficient Privacy Preserving Data Aggregation Research Articles

PADP: Efficient Privacy-Preserving Data Aggregation and Dynamic Pricing for Vehicle-to-Grid Networks

With the fast development of Internet of Things (IoT) especially for smart grid and electric vehicle (EV) networking, vehicle-to-grid (V2G) communications have been increasingly studied and recognized as one of the most convincing tools for general road transportation, to effectively reduce the oil demands and gas emissions. Unfortunately, a series of security and privacy issues have significantly impeded its wide adoption. The existing work mainly focused on the static environment, which cannot be directly applied to the mobile setting where EVs travel across regions. The dynamic pricing metric in V2G networks depends on the real-time electricity usage aggregation in one region. To address this issue, in this article, an efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption. In the proposed threshold homomorphic encryption, a legal ciphertext can be generated if and only if no less than threshold k individual illegal ciphertexts are aggregated. Therefore, the aggregated power consumption data can be successfully decrypted while the individual power consumption privacy of honest EV users can be well protected against even the collusion between a malicious power charging station and compromised EVs. Furthermore, the technique of SASD guarantees entity authentication with a minimized amount of transmitted data. Finally, formal security proof and extensive performance evaluation demonstrate the effectiveness and practicability of our proposed PADP.

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.

Security and Privacy for Cloud-Based IoT: Challenges

The Internet of Things is increasingly becoming a ubiquitous computing service, requiring huge volumes of data storage and processing. Unfortunately, due to the unique characteristics of resource constraints, self-organization, and shortrange communication in IoT, it always resorts to the cloud for outsourced storage and computation, which has brought about a series of new challenging security and privacy threats. In this article, we introduce the architecture and unique security and privacy requirements for the next generation mobile technologies on cloud-based IoT, identify the inappropriateness of most existing work, and address the challenging issues of secure packet forwarding and efficient privacy preserving authentication by proposing new efficient privacy preserving data aggregation without public key homomorphic encryption. Finally, several interesting open problems are suggested with promising ideas to trigger more research efforts in this emerging area.

An efficient privacy preserving data aggregation approach for mobile sensing

The advances in sensing capabilities of smartphones give rise to a variety of mobile participatory sensing applications that collect users' personal data. Because of the existence of both sensitive, private personal data, and untrusted aggregator, serious privacy concerns on users arise. Currently, existing privacy preserving data collection methods either require bidirectional communications between an untrusted aggregator and mobile users in every aggregation period, or have high computation or communication overhead. To address these problems, we propose an efficient data aggregation approach by which an untrusted aggregator in mobile sensing can collect the statistics over the data contributed by multiple mobile users, while supporting privacy preservation of each user and data integrity verification. In this approach, information hiding and homomorphic encryption are applied to guarantee the data privacy of mobile users. In detail, a breadth-first search tree is first constructed at the initial phase among the mobile users, and then the original datum of each user is perturbed among its neighbors in ciphertext space by using information hiding and homomorphic encryption. The evaluations of our approach show that our protocol requires lower communication and computation overhead and thus more feasible for the computation constrained mobile devices. Copyright © 2016 John Wiley & Sons, Ltd.

Research of privacy-preserving data aggregation algorithm for wireless sensor network

With the development of wireless sensor networks (WSNs), many applications require for the privacy preserving. Providing an efficient privacy–preserving data aggregation in WSNs is becoming more and more important. The secure data aggregation in WSNs has been studied in recent years, but most of the algorithms are suffering from the high computation overhead and high communication overhead, and some of the algorithms do not supply the data integrity checking. To resolve these problems, a modified private–preserving data aggregation based on complex number algorithm for WSN is proposed in this paper. An efficient key distribution scheme is used in the algorithm to improve the security of the key. The complex number is used in the algorithm to protect the privacy and check the data integrity. According to the analysis in the paper, the algorithm is efficient in terms of communication, computation and integrity checking.

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.