Privacy-Preserving Association Rule Mining Using Homomorphic Encryption in a Multikey Environment

Pang, Hongping; Wang, Baocang

doi:10.1109/jsyst.2020.3001316

Cited by 34 publications

(47 citation statements)

References 24 publications

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“…It also supports naive Bayes disease classification over the encrypted data at the fog node; however, it does not effectively support processing over data from multiple users. Pang and Wang [39] propose privacy preserving data mining operations on outsourced data from multiple parties under multi-key environments using VP-HE. The proposal supports sharing of processed data only with a data recipient (miner); however, it does not support user-centric and fine-grained data sharing with multiple users.…”

Section: Related Workmentioning

confidence: 99%

“…This section reviews briefly the Paillier cryptosystem [32], the Variant-Paillier in Multikey cryptosystem [39], and CP-ABE [34], which are used in the SAMA scheme design. The notations used throughout the paper are presented in Table II.…”

Section: Building Blocksmentioning

confidence: 99%

“…2) Variant-Paillier in Multi-Key Environment: The variant Paillier scheme [39] is one of the recent variations of the Paillier cryptosystem. It is similar to the original scheme [32] with a slight modification in the key generation algorithm, which makes it compatible to work in multiple users environment by generating a different public-private key pair for each user with two trapdoor decryption algorithms.…”

Section: A Paillier Cryptosystemmentioning

confidence: 99%

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Efficient Privacy-Friendly and Flexible IoT Data Aggregation with User-Centric Access Control

Jastaniah¹,

Zhang²,

Mustafa³

2022

Preprint

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This paper proposes a novel Single and Multiple user(s) data Aggregation (SAMA) scheme designed to support privacy preserving aggregation of health data collected from users' IoT wearables. SAMA also deploys a user-centric approach to support flexible fine-grain access control. It achieves this by deploying two key ideas. First, it uses multi-key Homomorphic cryptosystem (variant Paillier) to allow flexibility in accommodating both single and multi-user data processing as well as preserving the privacy of users while processing their IoT health data. Second, it uses ciphertext-policy attribute-based encryption to support flexible access control, which ensures users are able to grant data access securely and selectively. Formal security and privacy analyses have shown that SAMA supports data confidentiality and authorisation. The scheme has also been analysed in terms of computational and communication overheads to demonstrate that it is more efficient than the relevant state-of-the-art solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Building Blocksmentioning

confidence: 99%

Section: A Paillier Cryptosystemmentioning

confidence: 99%

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Efficient Privacy-Friendly and Flexible IoT Data Aggregation with User-Centric Access Control

Jastaniah¹,

Zhang²,

Mustafa³

2022

Preprint

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“…Pang and Wang (Pang & Wang, 2020) developed novel homomorphic cryptosystem that supports multiple cloud users with different public keys. In this work, secure association rule mining scheme is used with outsourced databases from multiple parties in a twin-cloud architecture.…”

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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“…Wang et al [14] devised a blocking based rule hiding that replaces data by unknowns such that patterns containing identified items on the antecedent part of the rule are not generated during the ARM process. Homomorphic encryption algorithm [15] conceals sensitive association rules in the outsourced data that is uploaded by multiple data owners. The key disadvantage of the heuristic technique is that it fails to provide an optimal solution to the data hiding problem in the vast majority of circumstances.…”

Section: Related Workmentioning

confidence: 99%

Privacy preserving association rule hiding using border based approach

Suma

Shobha

2021

IJEECS

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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Privacy-Preserving Association Rule Mining Using Homomorphic Encryption in a Multikey Environment

Cited by 34 publications

References 24 publications

Efficient Privacy-Friendly and Flexible IoT Data Aggregation with User-Centric Access Control

Efficient Privacy-Friendly and Flexible IoT Data Aggregation with User-Centric Access Control

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

Privacy preserving association rule hiding using border based approach

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