Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Efficient Privacy-Preserving Cube-Data Aggregation Scheme for Smart Grids

Abstract: Efficient power management in smart grids requires obtaining power consumption data from each resident. However, data concerning user’s electricity consumption might reveal sensitive information, such as living habits and lifestyles. In order to solve this problem, this paper proposes a privacy-preserving cube-data aggregation scheme for electricity consumption. In our scheme, a data item is described as a multi-dimensional data structure ( $l$ -dimensional), and users form and live in multiple residential areas ( $m$ areas, and at most $n$ users in each area). Based on Horner’s Rule, for each user, we construct a user-level polynomial to store dimensional values in a single data space by using the first Horner parameter. After embedding the second Horner parameter into the polynomial, the polynomial is hidden by using Paillier cryptosystem. By aggregating data from $m$ areas, we hide the area-level polynomial into the final output. Moreover, we propose a batch verification scheme in multi-dimensional data to reduce authentication cost. Finally, our analysis shows that the proposed scheme is efficient in terms of computation and communication costs, suitable for massive user groups, and supports the flexible and rapid growth of residential scales in smart grids.

Secure and privacy-preserving information aggregation for smart grids

Abstract: In this paper, we present a distributed incremental data aggregation approach, in which data aggregation is performed at all smart meters involved in routing the data from the source meter to the collector unit. With a carefully constructed aggregation tree, the aggregation route covers the entire local neighbourhood or any arbitrary set of designated nodes with minimum overhead. To protect user privacy, homomorphic encryption is used to secure the data enroute. Therefore, all the meters participate in the aggregation, without seeing any intermediate or final result. In this way, our approach supports efficient data aggregation in smart grids, while fully protecting user privacy. This approach is especially suitable for smart grids with repetitive routine data aggregation tasks.

On the Potential of Structure-Free Data Aggregation in Sensor Networks

Abstract: Data aggregation protocols can reduce the cost of communication, thereby extending the lifetime of sensor networks. Prior work on data aggregation protocols has focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms Data-Aware Anycast at the MAC layer and Randomized Waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structure-free data aggregation.

MuDA: Multifunctional data aggregation in privacy-preserving smart grid communications

Abstract: Privacy-preserving data aggregation has been widely studied to meet the requirement of timely monitoring electricity consumption of users while protecting individual user’s data privacy in smart grid communications. In this paper, we propose a new multifunctional data aggregation scheme, named MuDA, for privacy-preserving smart grid communications. With MuDA, the smart grid control center can compute multiple statistical functions of users’ data in a privacy-preserving way to provide diversiform services. Moreover, MuDA is also designed to resist differential attacks that most secure data aggregation schemes may suffer. Through detailed security and utility analyses, we demonstrate that MuDA preserves users’ data privacy with acceptable noise rate. In addition, extensive performance evaluations are conducted to illustrate that our MuDA scheme is more efficient than a popular aggregation scheme in terms of communication overhead.