Privacy-Preserving Mining of Association Rules for Smart Grid

2014 ◽  
Vol 971-973 ◽  
pp. 1692-1695
Author(s):  
Huan Ren ◽  
Lu Li ◽  
Liu Sheng Huang ◽  
Wei Yang
Keyword(s):  
Data Mining ◽  
Smart Grid ◽  
Association Rules ◽  
Distribution Network ◽  
Privacy Preserving ◽  
Electricity Distribution ◽  
Individual Data ◽  
Smart Meters ◽  
Privacy Concerns ◽  
The Individual

The widespread deployment of smart meters for the modernization of the electricity distribution network has been associated with privacy concerns due to the potentially large number of measurements that reflect the consumers’ behavior. At the same time, how to extract important knowledge from the potentially large of measurements — these measurements are spilt among various parties, has already became a hot topic in the field of data mining. In this paper, we present protocols that can be used to compute meter measurements over defined sets of meters without revealing any additional about the individual meter readings, and address secure mining of association rules. Thus, most of the benefits of the smart grid can be achieved without revealing individual data.

scholarly journals Distributed FP Growth Algorithm for Cloud Platform without Exposing the Individual Transaction Data

2019 ◽  
Vol 8 (3) ◽  
pp. 4983-4989
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Distributed Database ◽  
Privacy Preserving ◽  
Current Paper ◽  
Cloud Platform ◽  
Transaction Data ◽  
Powerful Technique ◽  
Hidden Patterns ◽  
The Individual

Data mining is a concept of extracting the required patterns to take appropriate decisions. One of the major challenges in data mining is to extract hidden patterns with the secure and privacy from the huge databases. Privacy preserving is a method used to extract hidden patterns with privacy. In this paper Mining Association rules with privacy preserving mechanism in the cloud platform is proposed. It is a powerful technique to find the hidden pattern in the distributed database. For now many mechanisms has proposed but it has many drawback, not proven and not specific. In cloud the data is stored in the servers. The data is distributed in different servers in cloud platform. Each server has one of the transaction data. The current paper proposed the distributed FP growth algorithm for cloud platform without exposing the individual transaction data. The results proved that the proposed algorithm is best to extract hidden pattern from Cloud platform in terms of efficiency.

Privacy-Preserving Data Mining

2008 ◽  
pp. 693-704
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Inference Problem ◽  
Privacy Preserving Data Mining ◽  
Privacy Concerns ◽  
Security Applications ◽  
Constraint Processing ◽  
Privacy Constraints ◽  
Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

2014 ◽  
Vol 23 (05) ◽  
pp. 1450004 ◽  
Author(s):  
Ibrahim S. Alwatban ◽  
Ahmed Z. Emam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Area ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Comprehensive Survey ◽  
New Research

In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.

scholarly journals Implementation of Automatic Power Consumption Control in Smart Grid

2021 ◽  
Vol 9 (VII) ◽  
pp. 2138-2143
Author(s):  
Anitha. A
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Power Supply ◽  
Smart Meters ◽  
Good Communication ◽  
Communications Network ◽  
Load Limit ◽  
Proper Design ◽  
The Individual ◽  
Consumption Control

Transformation to Smart Grid needs proper design of good communication and monitoring infrastructure for the Smart meters as well as understanding the power use pattern of the individual users for providing them uniform power supply as per the individual consumer’s requirement.In the proposed system, the meter monitors and calculates the power and if the consumer exceeds the prescribed load limit it alarms. In case the consumer does not reduce his load meter automatically it cuts off the particular loads in consumer connection. GSM communications network are used to transfer electricity consumed data to the consumer as per programmed in the Arduino kit.

scholarly journals Hiding Sensitive Association Rules over Privacy Preserving Distributed Data Mining

2014 ◽  
Vol 9 (1) ◽  
pp. 59-72
Author(s):  
Alaa Khalil Jumaa ◽  
Sufyan T. F. Al-Janabi ◽  
Nazar Abedlqader Ali
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Privacy Preserving ◽  
Distributed Data Mining ◽  
Distributed Data

scholarly journals Privacy-Preserving Aggregation and Authentication of Multi-Source Smart Meters in a Smart Grid System

2017 ◽  
Vol 7 (10) ◽  
pp. 1007 ◽  
Author(s):  
Dongyoung Koo ◽  
Youngjoo Shin ◽  
Junbeom Hur
Keyword(s):  
Smart Grid ◽  
Privacy Preserving ◽  
Grid System ◽  
Smart Meters ◽  
Smart Grid System

Privacy-Preserving Data Mining

2008 ◽  
pp. 627-638 ◽  
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Inference Problem ◽  
Privacy Preserving Data Mining ◽  
Privacy Concerns ◽  
Security Applications ◽  
Constraint Processing ◽  
Privacy Constraints ◽  
Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

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