Data aggregator

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

A Cluster-Based Protocol to Enforce Integrity and Preserve Privacy in Data Aggregation

Abstract: Data fusion or information collection is one of the fundamental functions in the future cyber-physical systems. But, privacy concerns must be addressed and security must be assured in such systems. It is very challenging to achieve the synergy of privacy and integrity, because privacy preserving schemes try to hide or interfere with data, while integrity protection usually needs to enable peer monitoring or public access of the data. Therefore, privacy and integrity can be the conflicting requirements, one may barricade the implementation of the other.In this paper, we address both privacy of individual sensory data and integrity of aggregation result simultaneously by proposing a protocol called iCPDA, which piggybacks on a cluster-based privacy-preserving data aggregation protocol(CPDA). We implement the add-on feature to protect integrity of aggregation result. To show the efficacy and efficiency of the proposed scheme, we present simulation results. To the best of our knowledge, this paper is among the first protocols to preserve privacy and integrity in data aggregation.

Latency-minimizing data aggregation in wireless sensor networks under physical interference model

Abstract: Minimizing latency is of primary importance for data aggregation which is an essential application in wireless sensor networks. Many fast data aggregation algorithms under the protocol interference model have been proposed, but the model falls short of being an accurate abstraction of wireless interferences in reality. In contrast, the physical interference model has been shown to be more realistic and has the potential to increase the network capacity when adopted in a design. It is a challenge to derive a distributed solution to latency-minimizing data aggregation under the physical interference model because of the simple fact that global-scale information to compute the cumulative interference is needed at any node. In this paper, we propose a distributed algorithm that aims to minimize aggregation latency under the physical interference model in wireless sensor networks of arbitrary topologies. The algorithm uses O(K) time slots to complete the aggregation task, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. The key idea of our distributed algorithm is to partition the network into cells according to the value K, thus obviating the need for global information. We also give a centralized algorithm which can serve as a benchmark for comparison purposes. It constructs the aggregation tree following the nearest-neighbor criterion. The centralized algorithm takes O( logn) and O(log^3n) time slots when coupled with two existing link scheduling strategies, respectively (where n is the total number of nodes), which represents the current best algorithm for the problem in the literature. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.

Verifiable Privacy-Preserving Aggregation in People-Centric Urban Sensing Systems

Abstract: People-centric urban sensing systems (PC-USSs) refer to using human-carried mobile devices such as smartphones and tablets for urban-scale distributed data collection, analysis, and sharing to facilitate interaction between humans and their surrounding environments. A main obstacle to the widespread deployment and adoption of PC-USSs are the privacy concerns of participating individuals as well as the concerns about data integrity. To tackle this open challenge, this paper presents the design and evaluation of VPA, a novel peer-to-peer based solution to verifiable privacy-preserving data aggregation in PC-USSs. VPA achieves strong user privacy by letting each user exchange random shares of its datum with other peers, while at the same time ensures data integrity through a combination of Trusted Platform Module and homomorphic message authentication code. VPA can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max/Min, Median, Histogram, and Percentile with accurate aggregation results. The efficacy and efficiency of VPA are confirmed by thorough analytical and simulation results.

Consensus-Based Data-Privacy Preserving Data Aggregation

Abstract: Privacy-preserving data aggregation (DA) in ad hoc networks is a challenging problem, considering the distributed communication and control requirement, dynamic network topology, unreliable communication links, etc. Different from the widely used cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. We first propose a secure consensus-based DA algorithm that guarantees an accurate sum aggregation while preserving the privacy of sensitive data. Then, we prove that the proposed algorithm converges accurately and is $(\epsilon, \sigma)$ -data privacy, and the mathematical relationship between $\epsilon$ and $\sigma$ is provided. Extensive simulations have shown that the proposed algorithm has high accuracy and low complexity, and they are robust against network dynamics.