A New Differentially Private Data Aggregation With Fault Tolerance for Smart Grid Communications

Bao, Haiyong; Lu, Rongxing

doi:10.1109/jiot.2015.2412552

Cited by 123 publications

(71 citation statements)

References 25 publications

(67 reference statements)

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“…Internet of Things (IoT) is a generic infrastructure that connects different “things” (e.g, different physical objects) to Internet [2], [3], [5]. The smart grid is a typical application supported by IoT [4].…”

Section: Related Workmentioning

confidence: 99%

“…Internet of Things (IoT) is an integrated and critical infrastructure that incorporates information communication techniques with physical devices to automatically collect and exchange data, which can support different applications/services, such as smart grid, smart transportation system, smart cities, and others [2], [3], [4], [5]. As a typical application/service by IoT, the smart grid has a goal of providing efficient, reliable, and cost-effective electricity power service to users via the integration of modern information and communication technologies in the traditional power grid [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid

Griffith

et al. 2016

IEEE Internet Things J.

122

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Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid1.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid

Griffith

et al. 2016

IEEE Internet Things J.

122

View full text Add to dashboard Cite

show abstract

“…Significant efforts have been made on privacy-preserving aggregation for distributed time-series data and/or providing differential privacy for individual data streams [12]- [22]. The PASTE algorithm in [12] targets historical time-series data and requires the pre-processing of all possible query results, so it cannot be applied for distributed real-time monitoring tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the framework in [4] enables monitoring arbitrary threshold functions over the MEAN aggregate of the statistics from distributed data-stream sources in a differentially privacypreserving fashion. In addition, references [17]- [22] further offer fault tolerance against sensor failure. All these work focuses on additive aggregation and thus cannot be applied to our problem.…”

Section: Related Workmentioning

confidence: 99%

PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Sun

Zhang

et al. 2016

IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications

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Distributed stream monitoring has numerous potential applications in future smart cities. Communication efficiency and data privacy are two main challenges for distributed stream monitoring services. In this paper, we propose PriStream, the first communication-efficient and privacy-preserving distributed stream monitoring system for thresholded PERCENTILE aggregates. PriStream allows the monitoring service provider to evaluate an arbitrary function over a desired percentile of distributed data reports and monitor when the output exceeds a predetermined system threshold. Detailed theoretical analysis and evaluations show that PriStream has high accuracy and communication efficiency, and differential privacy guarantees under a strong adversary model.

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“…However, in practice, the tendency is to share the ICT layer for minimizing the cost of installation, operation, maintenance, etc. [1][2][3]. The specific advantage comes with the drawback of increased vulnerability since a successful attack on the ICT layer may provide control to multiple CIs.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids

Ntalampiras

Soupionis

2016

Electronics

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Abstract:The thematics focusing on inserting intelligence in cyber-physical critical infrastructures (CI) have been receiving a lot of attention in the recent years. This paper presents a methodology able to differentiate between the normal state of a system composed of interdependent infrastructures and states that appear to be normal but the system (or parts of it) has been compromised. The system under attack seems to operate properly since the associated measurements are simply a variation of the normal ones created by the attacker, and intended to mislead the operator while the consequences may be of catastrophic nature. Here, we propose a holistic modeling scheme based on Gaussian mixture models estimating the probability density function of the parameters coming from linear time invariant (LTI) models. LTI models are approximating the relationships between the datastreams coming from the CI. The experimental platform includes a power grid simulator of the IEEE 30 bus model controlled by a cyber network platform. Subsequently, we implemented a wide range of integrity attacks (replay, ramp, pulse, scaling, and random) with different intensity levels. An extensive experimental campaign was designed and we report satisfying detection results.

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A New Differentially Private Data Aggregation With Fault Tolerance for Smart Grid Communications

Cited by 123 publications

References 25 publications

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid

PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids

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