Target-Based, Privacy Preserving, and Incremental Association Rule Mining

Ahluwalia, Madhu V.; Gangopadhyay, Aryya; Chen, Zhiyuan; Yesha, Yelena

doi:10.1109/tsc.2015.2484318

Cited by 12 publications

(4 citation statements)

References 37 publications

(52 reference statements)

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“…Various countermeasures (including secret-key dividing, ciphertext characterizing and mining share) are adopted to resist different kinds of attacks from the data owners, the cloud, and the outside adversary. Schemes [8], [11], [12] Schemes [6]- [9] Classic Solutions…”

Section: Main Contributionsmentioning

confidence: 99%

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

Lei

et al. 2022

IEEE Trans. Cloud Comput.

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Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose a Secure and Efficient Data Mining Outsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud's ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.

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Section: Main Contributionsmentioning

confidence: 99%

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

Lei

et al. 2022

IEEE Trans. Cloud Comput.

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“…The mining of strong fuzzy association rules can be divided into two steps: (i) generate frequent sets from fuzzy databases according to the D Min−supp ; (ii) generate candidate rule sets from frequent sets, calculate the D conf of each rule set, and obtain a strong fuzzy association rule set according to D Min−conf [14]. This process requires the entire database to be analysed whenever the D conf of a candidate rule is calculated, which results in several database scanning operations.…”

Section: Fuzzy Association Rule Miningmentioning

confidence: 99%

Transformer failure diagnosis using fuzzy association rule mining combined with case‐based reasoning

Guo

Wang

et al. 2020

IET Generation, Transmission & Distribution

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In the field of transformer failure diagnosis, the potential correlation between different characteristic parameters and failures is difficult to detect using traditional methods. Further, the quantities of inspection data have not been fully utilized. To improve the accuracy of transformer diagnosis, this study establishes a diagnosis model based on fuzzy association rules combined with case-based reasoning (CBR) to evaluate the failure types, fault locations, and cause of breakdown in power transformers. First, the inspection data of transformers are collected from several substations over 10 years. Then, the preprocessed data are randomly separated into training and testing sets. For the training set, fuzzy association rules are built for multiple parameters to narrow the search scope of base case preliminarily. Next, CBR is applied to determine the most similar cases. The failure information of the target transformer can be obtained in detail along with the most similar base case. Finally, the accuracy of the model is validated shown in case studies using the testing data set. The result demonstrates that the diagnosis model provides a higher accuracy than the classic IEC 60599 three-ratio method used in the current industry, which means that this diagnosis model has better performance on diagnosis accuracy.

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“…Li et al (2016) developed an efficient homomorphic encryption scheme that rendered the protection to the data of the cloud owner, but there was no outsourced comparison method. Ahluwalia et al (2017) utilized the target-based privatized incremental rule update algorithm that provided a greater degree of efficiency and privacy. On the other hand, the accuracy of the rules was better for a minute and moderate update in the huge volumes of data.…”

Section: Literature Surveymentioning

confidence: 99%

Distributed elephant herding optimization for grid-based privacy association rule mining

Gopagoni

K²

2020

DTA

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

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Target-Based, Privacy Preserving, and Incremental Association Rule Mining

Cited by 12 publications

References 37 publications

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

SecEDMO: Enabling Efficient Data Mining with Strong Privacy Protection in Cloud Computing

Transformer failure diagnosis using fuzzy association rule mining combined with case‐based reasoning

Distributed elephant herding optimization for grid-based privacy association rule mining

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