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Data aggregator

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


Papers
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Journal ArticleDOI
TL;DR: This article describes two scenarios for outsourcing data aggregation services and presents a set of decentralized peer-to-peer protocols for supporting data sharing across multiple private databases while minimizing the data disclosure among individual parties.
Abstract: Advances in distributed service-oriented computing and Internet technology have formed a strong technology push for outsourcing and information sharing. There is an increasing need for organizations to share their data across organization boundaries both within the country and with countries that may have lesser privacy and security standards. Ideally, we wish to share certain statistical data and extract the knowledge from the private databases without revealing any additional information of each individual database apart from the aggregate result that is permitted. In this article, we describe two scenarios for outsourcing data aggregation services and present a set of decentralized peer-to-peer protocols for supporting data sharing across multiple private databases while minimizing the data disclosure among individual parties. Our basic protocols include a set of novel probabilistic computation mechanisms for important primitive data aggregation operations across multiple private databases such as max, min, and top k selection. We provide an analytical study of our basic protocols in terms of precision, efficiency, and privacy characteristics. Our advanced protocols implement an efficient algorithm for performing kNN classification across multiple private databases. We provide a set of experiments to evaluate the proposed protocols in terms of their correctness, efficiency, and privacy characteristics.

68 citations

Journal ArticleDOI
TL;DR: A privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption and security analysis shows that PTAA can achieve the desirable security goals.
Abstract: Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g., sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency.

66 citations

Proceedings ArticleDOI
15 Sep 2008
TL;DR: Simulation results based on realistic map data and traffic models demonstrate that the design of a cooperative model to facilitate the aggregation of adjacent traffic reports can effectively reduce communication overhead with acceptable delay.
Abstract: In-network data aggregation is a useful technique to reduce redundant data and improve communication efficiency. One challenge in data aggregation is how reports can be routed to the same node so that the reports can be merged. Most of existing approaches rely on maintaining a routing structure to achieve this purpose. However, these approaches are not applicable to the mobile environment of Vehicular Ad hoc Networks (VANETs). In this paper, we design a cooperative model to facilitate the aggregation of adjacent traffic reports. The basic idea behind this work is that we can adaptively change the forwarding delay of individual reports in a manner that a report can have a better chance to meet other reports. The decision is made distributedly by each vehicle based on local observations. Actually, our scheme is also a tradeoff between communication overhead and propagation delay. Simulation results based on realistic map data and traffic models demonstrate that our scheme can effectively reduce communication overhead with acceptable delay.

65 citations

Journal ArticleDOI
TL;DR: A privacy-preserving data aggregation scheme with a flexibility property uses ElGamal Cryptosystem is proposed and is proved to be secure, private, and flexible with the analysis and performance simulation.
Abstract: The development of the Internet of Things (IoT) and 5th generation wireless network (5G) is set to push the smart agriculture to the next level since the massive and real-time data can be collected to monitor the status of crops and livestock, logistics management, and other important information. Recently, COVID-19 has attracted more human attention to food safety, which also has a positive impact on smart agriculture market share. However, the security and privacy concern for smart agriculture has become more prominent. Since smart agriculture implies working with large sets of data, which usually sensitive, some are even confidential, and once leakage it can expose user privacy. Meanwhile, considering the data publishing of smart agriculture helps the public or investors to real-timely anticipate risks and benefits, these data are also a public resource. To balance the data publishing and data privacy, in this article, a privacy-preserving data aggregation scheme with a flexibility property uses ElGamal Cryptosystem is proposed. It is proved to be secure, private, and flexible with the analysis and performance simulation.

65 citations

Journal ArticleDOI
TL;DR: This paper proposes a privacy-friendly and efficient data aggregation scheme for dynamic pricing-based billing and demand-response management in smart grids, and is the first paper to address privacy in the context of billing under dynamic electricity pricing.
Abstract: Smart grids take advantage of information and communication technologies to achieve energy efficiency, automation, and reliability. These systems allow two-way communications and power flow between the grid and consumers. However, these bidirectional communications introduce several security and privacy threats to consumers. One of the open challenges in this context is user privacy when smart meters (SMs) are used to capture fine-grained energy usage information. Although considerable research has been carried out in this direction, most of the existing solutions invariably introduce computational complexity and overhead, which makes them infeasible for resource constrained SMs. In this paper, we propose a privacy-friendly and efficient data aggregation scheme for dynamic pricing-based billing and demand-response management in smart grids. To the best of our knowledge, this is the first paper to address privacy in the context of billing under dynamic electricity pricing. Security and performance analyses show that the proposed scheme offers better privacy protection for electric meter reading aggregation and computational efficiency, as compared to existing schemes.

65 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023104
2022277
2021189
2020207
2019179
2018188