Association Rule Hiding Using Chemical Reaction Optimization

Gopalan, N. P.; Murthy, T. Satyanarayana

doi:10.1007/978-981-13-1592-3_19

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In particular, they modified sensitive items to protect the linking rules and used dummy item creation to keep the same cost for the original and new databases. Gopalan et al [53] developed an efficient meta-heuristic algorithm for association rule hiding based on a chemical reaction optimization algorithm. The results of the proposed approach are compared with the genetic algorithm, particle swarm optimization, and cuckoo-based algorithms.…”

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

2020

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The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

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Section: Privacy-preserving Data Miningmentioning

confidence: 99%

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

2020

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“…T.Satyanarayana Murthy [21,22] proposed novel algorithms for privacy preserving data mining. N.P.Gopalan, T.Satyanarayana Murthy [23] uses CRO for privacy preserving.N.P.Gopalan, T.Satyanarayana Murthy, Yalla Venkateswarlu [24] uses a novel approach for hiding critical transactions using Un-realization Approach.T.Satyanarayana Murthy et al [25] proposed an efficient method for hiding association rules with additional parameter metrics.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Algorithm for Association Rule Hiding

Murthy¹,

Gopalan²

2018

IJIEEB

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Current days privacy concern about an individual, an organization and social media etc. plays a vital role. Online business deals with millions of transactions daily, these transactions may leads to privacy issues. Association rule hiding is a solution to these privacy issue, which focuses on hiding the sensitive information produces from online departmental stores ,face book datasets etc..These techniques are used to identify the sensitive rules and provide the privacy to the sensitive rules, so that results the lost rules and ghost rules. Algorithms developed so far are lack in achieving the better outcomes. This paper propose two novel algorithm that uses the properties from genetic algorithm and water marking algorithm for better way of hiding the sensitive association rules.

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“…ID3 algorithmic approach took huge computational time in construction of decision tree. Satya proposed un-realization algorithms for association rule hiding [14][15][16][17][18][19][20][21][22][23][24]. In this manuscript, a modified un-realization algorithm and a modified classification and regression tree based algorithms are proposed for privacy preserving decision tree learning to reduce the computational time in decision tree construction process.…”

Section: Introductionmentioning

confidence: 99%

An Un-realized Algorithm for Effective Privacy Preservation Using Classification and Regression Trees

Teki¹,

Banothu²,

Varma³

2019

RIA

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In this digital age, privacy preservation has attracted much attention, as a huge amount of data are generated from multiple sources and transmitted across the Internet. Several perturbation algorithms have emerged to keep sensitive data hidden behind additive noises. In this paper, a novel un-realization algorithm is developed based on a classification and regression tree (CART). First, the sample dataset was distorted, and the duplicate elements were removed, creating a perturbed dataset and an un-realized dataset. Then, a decision tree was set up by the modified CART algorithm and another by the traditional CART based on the un-realized dataset. Finally, the Gini values of the two trees were compared. If the results are the same, then the privacy of the data is preserved. The proposed algorithm was compared with several traditional un-realization algorithms through experiments. The results show that our algorithm achieved excellent results in Gini value, time complexity and output accuracy.

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Association Rule Hiding Using Chemical Reaction Optimization

Cited by 7 publications

References 15 publications

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

A Novel Algorithm for Association Rule Hiding

An Un-realized Algorithm for Effective Privacy Preservation Using Classification and Regression Trees

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