Private Computation of Spatial and Temporal Power Consumption with Smart Meters

Erkin, Zekeriya; Tsudik, Gene

doi:10.1007/978-3-642-31284-7_33

Cited by 143 publications

(165 citation statements)

References 16 publications

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“…In [6], the authors proposed a protocol for secure aggregation of data using a modified version of Paillier homomorphic encryption. The aggregator which is interested in learning the aggregate sum of data is able to decrypt without knowing the decryption key.…”

Section: Dynamic Group Managementmentioning

confidence: 99%

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Leontiadis

Elkhiyaoui

Molva

2014

Lecture Notes in Computer Science

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Abstract. With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [8] recently suggested a solution that allows a centralized party to compute the sum of encrypted inputs collected through a smart metering network. The main shortcomings of this solution are its reliance on a trusted dealer for key distribution and the need for frequent key updates. In this paper we introduce a secure protocol for aggregation of timeseries data that is based on the Joye et al.[8] scheme and in which the main shortcomings of the latter, namely, the requirement for key updates and for the trusted dealer are eliminated. Moreover our scheme supports a dynamic group management, whereby as opposed to Joye et al.[8] leave and join operations do not trigger a key update at the users.

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Section: Dynamic Group Managementmentioning

confidence: 99%

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Leontiadis

Elkhiyaoui

Molva

2014

Lecture Notes in Computer Science

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“…****In the homomorphic encryption-based approaches discussed in [3,18,26,48,49,50], we observe that the power-usage information is generally of small size (e.g. 20 bits) [4,52].…”

Section: Discussionmentioning

confidence: 99%

“…20 bits) [4,52]. However, the plaintext input size of most existing homomorphic cryptosystems is huge [3,52], for example 2048 bits for the widely-used Paillier cryptosystem [42,48,50,52]. As a result, the input data has to be padded before encryption and the size of the output is also large.…”

Section: Discussionmentioning

confidence: 99%

“…In their second approach, named comparison protocols, they consider that the UC roughly knows the total consumption. Erkin and Tsudik [50] propose a cryptographic protocol based on a modified version of the Paillier cryptosystem to calculate the total consumption of all the SMs in a given neighborhood as well as a single SM in the AMI. Acs and Castelluccia [51] suggest a solution using masking and differential privacy and utilizing the homomorphic properties of a computationally-cheap cryptosystem for private data aggregation.…”

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confidence: 99%

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Towards Efficient Privacy Preserving Data Aggregation for Advanced Metering Infrastructure

Alamatsaz¹,

Boustani²,

Alamatsaz³

et al. 2017

IJCNC

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Recent changes to the existing power grid are expected to influence the way energy is pro-vided and consumed by customers. Advanced Metering Infrastructure (AMI) is a tool to incorporate these changes for modernizing the electricity grid. Growing energy needs are forcing government agencies and utility companies to move towards AMI systems as part of larger smart grid initiatives. The smart grid promises to enable a more reliable, sustainable, and efficient power grid by taking advantage of information and communication technologies. However, this information-based power grid can reveal sensitive private information from the user's perspective due to its ability to gather highly-granular power consumption data. This has resulted in limited consumer acceptance and proliferation of the smart grid. Hence, it is crucial to design a mechanism to prevent the leakage of such sensitive consumer usage information in smart grid. Among different solutions for preserving consumer privacy in Smart Grid Networks (SGN), private data aggregation techniques have received a tremendous focus from security researchers. Existing privacy-preserving aggregation mechanisms in SGNs utilize cryptographic techniques, specifically homomorphic properties of public-key cryptosystems. Such homomorphic approaches are bandwidth-intensive (due to large output blocks they generate), and in most cases, are computationally complex. In this paper, we present a novel and efficient CDMA-based approach to achieve privacy-preserving aggregation in SGNs by utilizing random perturbation of power consumption data and with limited use of traditional cryptography. We evaluate and validate the efficiency and performance of our proposed privacy-preserving data ag-gregation scheme through extensive statistical analyses and simulations.

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“…Our work builds upon two main domains, in order to provide the privacy and incentives for the users and data aggregators: (1) privacy-preserving aggregation [14,36,37,46], and (2) privacy-preserving monetization of user profiles [4,19,35,41]. Hereafter we discuss these two sets of works.…”

Section: Related Workmentioning

confidence: 99%

What’s the Gist? Privacy-Preserving Aggregation of User Profiles

Bilogrevic

Freudiger²,

Cristofaro

et al. 2014

Lecture Notes in Computer Science

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Over the past few years, online service providers have started gathering increasing amounts of personal information to build user profiles and monetize them with advertisers and data brokers. Users have little control of what information is processed and are often left with an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model -the "gist" -of their data. We aim to preserve data utility and simultaneously provide user privacy. We show that this approach can be efficiently supported by letting users contribute encrypted and differentially-private data to an aggregator. The aggregator combines encrypted contributions and can only extract an aggregate model of the underlying data. We evaluate our framework on a dataset of 100,000 U.S. users obtained from the U.S. Census Bureau and show that (i) it provides accurate aggregates with as little as 100 users, (ii) it generates revenue for both users and data brokers, and (iii) its overhead is appreciably low.

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Private Computation of Spatial and Temporal Power Consumption with Smart Meters

Cited by 143 publications

References 16 publications

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Towards Efficient Privacy Preserving Data Aggregation for Advanced Metering Infrastructure

What’s the Gist? Privacy-Preserving Aggregation of User Profiles

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