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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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Proceedings ArticleDOI
25 Mar 2012
TL;DR: This paper identifies a new security threat in collaborative sensing from testbed implementation, and it is shown that the attackers could geo-locate a secondary user from its sensing report with a successful rate of above 90% even in the presence of data aggregation.
Abstract: Collaborative spectrum sensing has been regarded as a promising approach to enable secondary users to detect primary users by exploiting spatial diversity. In this paper, we consider a converse question: could space diversity be exploited by a malicious entity, e.g., an external attacker or an untrusted Fusion Center (FC), to achieve involuntary geolocation of a secondary user by linking his location-dependent sensing report to his physical position. We answer this question by identifying a new security threat in collaborative sensing from testbed implementation, and it is shown that the attackers could geo-locate a secondary user from its sensing report with a successful rate of above 90% even in the presence of data aggregation. We then introduce a novel location privacy definition to quantify the location privacy leaking in collaborative sensing. We propose a Privacy Preserving collaborative Spectrum Sensing (PPSS) scheme, which includes two primitive protocols: Privacy Preserving Sensing Report Aggregation protocol (PPSRA) and Distributed Dummy Report Injection Protocol (DDRI). Specifically, PPSRA scheme utilizes applied cryptographic techniques to allow the FC to obtain the aggregated result from various secondary users without learning each individual's values while DDRI algorithm can provide differential location privacy for secondary users by introducing a novel sensing data randomization technique. We implement and evaluate the PPSS scheme in a real-world testbed. The evaluation results show that PPSS can significantly improve the secondary user's location privacy with a reasonable security overhead in collaborative sensing.

108 citations

Journal ArticleDOI
TL;DR: A novel integrity protecting hierarchical concealed data aggregation protocol that allows the aggregation of data packets that are encrypted with different encryption keys and during the decryption of aggregation, the base station is able to classify the encrypted and aggregated data based on the encryption keys.

108 citations

Journal ArticleDOI
TL;DR: This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data in IoT-WSN systems.

105 citations

Proceedings ArticleDOI
26 Sep 2004
TL;DR: Simulation results show that the proposed protocol yields significant savings in energy consumption while preserving data security, and also establishes secure connectivity among sensor nodes without any online key distribution.
Abstract: Data aggregation in wireless sensor networks eliminates data redundancy, thereby improving bandwidth usage and energy utilization. The paper presents a secure data aggregation protocol, called SRDA (secure reference-based data aggregation), for wireless sensor networks. In order to reduce the number of bits transmitted, sensor nodes compare their raw sensed data value with their reference data value and then transfer only the difference data. In addition to reducing the number of transmitted bits, SRDA also establishes secure connectivity among sensor nodes without any online key distribution. The security level of the communication links is gradually increased as packets are transmitted at higher level cluster-heads, since intercepting a packet at higher levels of the clustering hierarchy provides a summary of a large number of transmissions at lower levels. Simulation results show that the proposed protocol yields significant savings in energy consumption while preserving data security.

105 citations

Journal ArticleDOI
TL;DR: This paper considers how an external aggregator or multiple parties can learn some algebraic statistics over participants' privately owned data while preserving the data privacy, and proposes several protocols that successfully guarantee data privacy under semi-honest model.
Abstract: Much research has been conducted to securely outsource multiple parties’ data aggregation to an untrusted aggregator without disclosing each individual’s privately owned data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either require secure pair-wise communication channels or suffer from high complexity. In this paper, we consider how an external aggregator or multiple parties can learn some algebraic statistics (e.g., sum, product) over participants’ privately owned data while preserving the data privacy. We assume all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We first propose several protocols that successfully guarantee data privacy under semi-honest model, and then present advanced protocols which tolerate up to $k$ passive adversaries who do not try to tamper the computation. Under this weak assumption, we limit both the communication and computation complexity of each participant to a small constant. At the end, we present applications which solve several interesting problems via our protocols.

102 citations


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