Privacy-Preserving Data Collecting: A Simple Game Theoretic Approach

Xu, Lei; Jiang, Chunxiao; Qian, Yi; Ren, Yong

doi:10.1007/978-3-319-77965-2_2

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The privacy requirements should be specified for collecting, storing and processing the big data. [22] There are different ways to secure the data and more ways are being introduced. One of them is cryptography and it is the frequently used method.…”

Section: Data Privacymentioning

confidence: 99%

The Overview of Security Measures in Big Data

Munir

Nadeem

Zahra

et al. 2021

IJECI

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Big data refers to the huge volume of data that is being produced by different organizations after every second. It is hard to handle the such volume of data, therefore this data is combined and managed through the servers of organizations using complex algorithms. Data is currently the most important asset of any organization in every field with which many operations can be performed. Big Data exerts different properties those are volume, velocity, veracity, and variety. Now there are different threats regarding the security of big data. Organizations are using different methods to secure big data. Big data security measures include architecture security, infrastructure security, and data privacy. In this paper multiple security concerns are discussed and how they can help organizations secure their data.

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Section: Data Privacymentioning

confidence: 99%

The Overview of Security Measures in Big Data

Munir

Nadeem

Zahra

et al. 2021

IJECI

View full text Add to dashboard Cite

show abstract

“…To overcome the problem of computation accuracy reduction, an approximation method is proposed in [21] which leads to obtain a closed form of aggregators decision problem. In [34], a slightly different scenario is considered in which a data aggregator collects data from consumers and then spreads data to supplier. The goal is to preserve consumers data privacy.…”

Section: Related Workmentioning

confidence: 99%

Statistical-Based Privacy-Preserving Scheme with Malicious Consumers Identification for Smart Grid

Ahadipour,

Mohammadi,

Keshavarz-Haddad

2019

Preprint

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As smart grids are getting popular and being widely implemented, preserving the privacy of consumers is becoming more substantial. Power generation and pricing in smart grids depends on the continuously gathered information from the consumers. However, having access to the data relevant to the electricity consumption of each individual consumer is in conflict with its privacy. One common approach for preserving privacy is to aggregate data of different consumers and to use their smartmeters for calculating the bills. But in this approach, malicious consumers who send erroneous data to take advantage or disrupt smart grid cannot be identified. In this paper, we propose a new statistical-based scheme for data gathering and billing in which the privacy of consumers is preserved, and at the same time, if any consumer with erroneous data can be detected. Our simulation results verify these matters.

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Privacy-Preserving Data Collecting: A Simple Game Theoretic Approach

Cited by 2 publications

References 9 publications

The Overview of Security Measures in Big Data

The Overview of Security Measures in Big Data

Statistical-Based Privacy-Preserving Scheme with Malicious Consumers Identification for Smart Grid

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