Topic
Data aggregator
About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.
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20 Oct 2020
TL;DR: Wang et al. as mentioned in this paper proposed a uniquely crafted privacy-preserving monitoring and billing scheme using functional encryption, referred to as PMBFE, which fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers' privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacypreservation.
Abstract: Preserving the customers’ privacy, while collecting their power consumption for monitoring and billing, is a prime concern in an Advanced Metering Infrastructure (AMI) network of the Smart Grid (SG). In this paper, we address this concern by formally formulating the data aggregation privacy problem, and propose a uniquely crafted Privacy-Preserving Monitoring and Billing scheme using Functional Encryption, referred to as PMBFE. Our proposed PMBFE fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers’ privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacy-preservation. Our envisioned PMBFE approach is evaluated with extensive computer-based simulations. In contrast with the widely employed homomorphic-based encryption in AMI networks, our proposed PMBFE demonstrates significant performance improvement in terms of both communication and computation overheads while guaranteeing user-data privacy. Furthermore, the conducted security analysis exhibits the robustness of our proposal against collusion and eavesdropping attacks.
35 citations
TL;DR: A novel framework is proposed for handling the local broadcast storm problem using probabilistic data aggregation which reduces the bandwidth consumption and hence improves the information dissemination and is evaluated for VANET based traffic information system through simulation for strictly limited bandwidth and local broadcast problem.
Abstract: Data aggregation is used to combine correlated data items from different vehicles before redistributing to other vehicles in the vehicular ad hoc networks (VANET). The number of retransmissions and the communication overhead can be reduced considerably by using aggregation. It is a prerequisite for applications that require periodic dissemination of information into a large region so that, drivers can be informed well in advance and can take alternative route in case of traffic congestion. Dissemination of information to vehicles through broadcasting creates a broadcast storm problem in VANET. In this paper a novel framework is proposed for handling the local broadcast storm problem using probabilistic data aggregation which reduces the bandwidth consumption and hence improves the information dissemination. This system exploits the knowledge base and stores the decisions for aggregation and is based on a flexible and extensible set of criteria. These criteria's can be application specific and can enable a dynamic fragmentation of the road according to the various application requirements. The framework is evaluated for VANET based traffic information system through simulation for strictly limited bandwidth and local broadcast problem. The results demonstrate that completely structure-free probabilistic data aggregation reduces the bandwidth consumption by eliminating the local broadcast problem.
35 citations
TL;DR: By observing the user registration procedure in Fan et al.'s scheme, it is shown that each user's private key can be easily derived from the information published by the aggregator, and data integrity will be completely violated.
Abstract: Quite recently, Fan et al. ( IEEE Trans. Ind. Informat. , vol. 10, no. 1, pp. 666–675, 2014) proposed a new data aggregation scheme for smart grid communications, and claimed that it can achieve not only user’s privacy-preservation, but also data integrity requirement. However, in this paper, we show that Fan et al. ’s scheme has a serious security flaw and cannot meet data integrity requirement at all. Specifically, by observing the user registration procedure in Fan et al. ’s scheme, we find that each user’s private key can be easily derived from the information published by the aggregator. Then, with the derived private key, an attacker can inject polluted data to user’s real data without being detected. As a result, data integrity will be completely violated. We hope that with our comment, similar mistakes can be avoided in future design of privacy-preserving data aggregation with data integrity protection.
35 citations
TL;DR: This paper proposes to the use cloud to compute a set operation for the requester, at the same time workers’ data privacy and identities privacy are well preserved, and extends the scheme to support data preprocessing, with which invalid data can be excluded before data analysis.
Abstract: The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester (task owner) can crowdsource data from the workers (smartphone users) by using their sensor-rich mobile devices. However, data collection, data aggregation, and data analysis have become challenging problems for a resource constrained requester when data volume is extremely large, i.e., big data. In particular to data analysis, set operations, including intersection, union, and complementation, exist in most big data analysis for filtering redundant data and preprocessing raw data. Facing challenges in terms of limited computation and storage resources, cloud-assisted approaches may serve as a promising way to tackle the big data analysis issue. However, workers may not be willing to participate if the privacy of their sensing data and identity are not well preserved in the untrusted cloud. In this paper, we propose to the use cloud to compute a set operation for the requester, at the same time workers’ data privacy and identities privacy are well preserved. Besides, the requester can verify the correctness of set operation results. We also extend our scheme to support data preprocessing, with which invalid data can be excluded before data analysis. By using batch verification and data update methods, the proposed scheme greatly reduces the computational cost. Extensive performance analysis and experiment based on real cloud system have shown both the feasibility and efficiency of our proposed scheme.
35 citations
TL;DR: This article constructs an efficient identity-based metering data aggregation scheme supporting batch verification by collector and electricity service provider, respectively, which guarantees the privacy and integrity of meteringData.
Abstract: Smart grid can greatly improve the efficiency, reliability, and sustainability of the traditional grids. In industrial smart grid, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. However, the procedure of frequently metering data collection may lead to sensitive information leakage. To address the security issues in industrial smart grid, in this article, we construct an efficient identity-based metering data aggregation scheme supporting batch verification by collector and electricity service provider, respectively, which guarantees the privacy and integrity of metering data. In our scheme, collectors are allowed to collect and aggregate the metering data of users in their respective administrative domain without compromising the confidentiality of metering data. Security analysis demonstrates that our proposed scheme is provably secure in the random oracle and satisfies the above security requirements. Performance analysis indicates that our scheme outperforms existing solutions in terms of communication and computation costs.
34 citations