Lossless and robust privacy preservation of association rules in data sanitization

Navale, Geeta S.; Mali, Suresh N.

doi:10.1007/s10586-018-2176-1

Cited by 12 publications

(6 citation statements)

References 44 publications

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“…This section discusses about four different privacy preservation models (Kalyani, Rao, & Janakiramaiah, 2017; Lin et al, 2016; Murthy, Roy, & Varma, 2020; Navale & Mali, 2018). In Navale and Mali (2018) an effective sanitizing algorithm was constructed by the authors for hiding the sensitive rules given by the experts/users. The Firefly optimization algorithm was utilized here with no modifications for reducing the four sanitization research challenges: hiding failure, information loss, false rule generation and modification degree.…”

Section: Resultsmentioning

confidence: 99%

Section: Analysis Of the Presented Privacy Preservation Work Over The Existing Privacy Preservation Work With Sanitization Based Optimizamentioning

confidence: 99%

“…As a whole the experimental analysis revealed that the data similarity between the original database and the sanitized database is higher and deletion or change in the transactions of the original database is also higher. The hiding failure rates recorded by Navale and Mali (2018); Lin et al (2016); Kalyani et al (2017); Murthy et al, 2020) and proposed ROOA are shown in Table 11.…”

Section: Analysis Of the Presented Privacy Preservation Work Over The Existing Privacy Preservation Work With Sanitization Based Optimizamentioning

confidence: 99%

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Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

Shivashankar¹,

Mary²

2021

Expert Systems

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Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality and privacy of the data gets lost. There is a need to conserve the data and make it available for reuse when needed. Hence, it needs a proficient method to maintain and protect individuals' data privacy regarding confidentiality and reliability. This paper intends to develop an advanced model for privacy preservation of huge data with the accomplishment of two stages, namely data sanitization and data restoration. Data sanitization process preserves the safety of sensitive data stored in huge databases, by means of hiding those sensitive data from unauthorized users. Data restoration is the process of recovering or restoring of data that is sanitized at the sender side. Concerning the secrecy, there is a need for an optimal key to hide the sensitive data at sender as well as receiver side. Subsequent to the data sanitization, it requires the same key to restore the sanitized data. Thus, the optimal key generation plays a vital role to maintain privacy preservation. In order to choose an optimal key, a modified Rider optimization Algorithm (ROA) named as Randomized ROA (RROA) model is implemented in this work. Furthermore, the efficiency of the proposed work is compared over the state‐of‐the‐arts models by concerning the sanitization as well as restoration efficiency.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of the Presented Privacy Preservation Work Over The Existing Privacy Preservation Work With Sanitization Based Optimizamentioning

confidence: 99%

Section: Analysis Of the Presented Privacy Preservation Work Over The Existing Privacy Preservation Work With Sanitization Based Optimizamentioning

confidence: 99%

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Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

Shivashankar¹,

Mary²

2021

Expert Systems

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“…The methods used for the comparison include the firefly algorithm (FA) (Navale and Mali, 2018), PSO (Krishnamoorthy et al , 2017), EHO (Wang et al , 2015) and improved apriori (Rehman and Sharma, 2017). The effectiveness of the proposed method is revealed through the comparative analysis that is progressed based on the performance metrics.…”

Section: Resultsmentioning

confidence: 99%

“…This section demonstrates the review of eight existing models associated with the data mining, and the importance of the methods is detailed along with the demerits of the methods, which assist the researcher in developing a better method for data mining. Navale and Mali (2018) developed an algorithm, an effective sanitizing algorithm that rendered the ability to generate the key with minimal length and adapted sanitization and restoration of the data with no data loss. However, the problem was regarding the protection of the sensitive data in the database.…”

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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Secured information sharing for supply chain management based on blockchain technology and optimal key generation process

Lahane¹,

Sharma

2022

Concurrency and Computation

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Summary Supply Chain Management (SCM) is one of the biggest important focal areas in most corporate environments. However, some difficulties are presented in the structure like high cost, privacy issues and distortions. Thus, new secured information sharing for SCM is proposed in this work and deals with the introduction of privacy preservation of data transfers among supply chains. Initially, a supply chain is constructed and the data transfer among the diverse levels takes place via blockchain technology. In the blockchain, security being a crucial factor, a new privacy preservation method is introduced. The initial information is converted to a sanitization procedure prior to transmission via blockchain technology, in which the information is turned into sanitized data. Similarly, the sanitized data is restored on the receiving end to obtain the original data. More importantly, key generation has a significant role in both processes, and it must be ideally chosen. The key is optimally chosen for this study using a new optimization algorithm called Fitness‐based Self‐adaptive Sea lion Optimization (FSA‐SLnO), which is a better variant of the conventional Sealion Optimization technique (SLnO). In terms of security, the proposed protected information sharing in SCM using block chain technology is evaluated by comparing it with traditional approaches.

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Lossless and robust privacy preservation of association rules in data sanitization

Cited by 12 publications

References 44 publications

Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

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

Secured information sharing for supply chain management based on blockchain technology and optimal key generation process

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