Association rules and deep learning for cryptographic algorithm in privacy preserving data mining

Rajesh, N.; Selvakumar, A. Arul Lawrence

doi:10.1007/s10586-018-1827-6

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…The methods use a secure multi-party computation technique to where several parties perform data mining analysis. To improve the quality of the sanitized database, a more recent approach in [19] proposes a cryptographic technique to hide sensitive rules in transaction databases. The method successfully protects the transaction database from inference attacks.…”

Section: Data Sanitization Methodsmentioning

confidence: 99%

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

Gunawan¹,

Nugroho²,

Maryam³

2021

IJACSA

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Sharing a transaction database with other parties for exploring valuable information becomes more recognized by business institutions, i.e., retails and supermarkets. It offers various benefits for the institutions, such as finding customer shopping behavior and frequently bought items, known as frequent itemsets. Due to the importance of such information, some institutions may consider certain frequent itemsets as sensitive information that should be kept private. Therefore, prior to handling a database, the institutions should consider privacy preserving data mining (PPDM) techniques for preventing sensitive information breaches. Presently, several PPDM methods, such as item suppression-based methods and item insertionbased methods have been developed. Unfortunately, the methods result in significant changes to the database and induce some side effects such as hiding failure, significant data dissimilarity, misses cost, and artificial frequent itemset occurrence. In this paper, we propose a swapping-based data sanitization method that can hide the sensitive frequent itemset while at the same time it can minimize the side effects of the data sanitization process. Experimental results indicate that the proposed method outperforms existing methods in terms of minimizing the side effects.

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Section: Data Sanitization Methodsmentioning

confidence: 99%

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

Gunawan¹,

Nugroho²,

Maryam³

2021

IJACSA

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“…Rajesh and Selvakumar (Rajesh & Selvakumar, 2019) developed a privacy preservation datamining scheme with data-mining perturbation merged approach for ensuring the data privacy. In this work, association rules with cryptography technique are used for data privacy and preservation.…”

Section: Related Workmentioning

confidence: 99%

Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned

Mewada¹

2021

International Journal of E-Health and Medical Communications

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The valuable information is extracted through data mining techniques. Recently, privacy preserving data mining techniques are widely adopted for securing and protecting the information and data. These techniques convert the original dataset into protected dataset through swapping, modification, and deletion functions. This technique works in two steps. In the first step, cloud computing considers a service platform to determine the optimum horizontal partitioning in given data. In this work, K-Means++ algorithm is implemented to determine the horizontal partitioning on the cloud platform without disclosing the cluster centers information. The second steps contain data protection and recover phases. In the second step, noise is incorporated in the database to maintain the privacy and semantic of the data. Moreover, the seed function is used for protecting the original databases. The effectiveness of the proposed technique is evaluated using several benchmark medical datasets. The results are evaluated using encryption time, execution time, accuracy, and f-measure parameters.

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“…How to discover these hidden and valuable relationships has always been a research hotspot in the field of data mining [ 1 , 2 ]. Among them, data mining technology refers to a large number of data sets, through association rule mining, classification, aggregation and outlier detection and other methods, to find the internal relationship between the data in the data set without obvious characteristics, so as to guide people in subsequent production activities.…”

Section: Introductionmentioning

confidence: 99%

Data Mining and Meta-Analysis of Psoriasis Based on Association Rules

Zhang

2022

Journal of Healthcare Engineering

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Psoriasis is a common chronic and recurrent disease in dermatology, which has a great impact on the physical and mental health of patients. Meta-analysis can evaluate the effectiveness and safety of defubao in the treatment of psoriasis vulgaris. This article observes psoriasis skin lesions treated with topical defubao and the changes in blood vessels under dermoscopy. Considering that the Apriori algorithm and the existing improved algorithm have the problems of ignoring the weight and repeatedly scanning the database, this paper proposes a matrix association rule method based on random forest weighting. This method uses the random forest algorithm to assign weights to each item in the data set, and introduces matrix theory to convert the transaction data set into a matrix form and store it, thereby improving operating efficiency. This article included 11 studies, of which 7 studies used the indicator “Researcher’s Overall Assessment” (IGA) to evaluate the efficacy, 5 studies used the “Patient Overall Assessment” (PGA) as the efficacy evaluation index, and Loss Area and Severity Index (PASI) was used as an observation index to evaluate the efficacy. Seven studies conducted safety comparisons. In this paper, IGA and PGA were used as evaluation indicators. The treatment effect of the defubao group was better than the calcipotriol group and the betamethasone group. The differences were statistically significant. The effect of the Fubao treatment for 8 weeks is significantly better than that of 4 weeks and 2 weeks, and the differences are statistically different. Using PASI as the evaluation index, a descriptive study was carried out, and it was found that after 4 weeks of treatment for psoriasis vulgaris, the average PASI reduction rate of patients was higher than that of the calcipotriol group and the betamethasone group. The safety evaluation found that after 8 weeks of treatment, the incidence of adverse events in the defubao group was significantly lower than that in the calcipotriol group.

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Association rules and deep learning for cryptographic algorithm in privacy preserving data mining

Cited by 13 publications

References 8 publications

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned

Data Mining and Meta-Analysis of Psoriasis Based on Association Rules

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