Secure Association Rule Mining on Vertically Partitioned Data Using Private-Set Intersection

Nomura, Kenta; Shiraishi, Yoshiaki; Mohri, Masami; Morii, Masakatu

doi:10.1109/access.2020.3014330

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…A Private Set Intersection (PSI) protocol [12] allows two or more parties, each holding a private set of items to compute the intersection of their sets (i.e., items that they have in common) without revealing any additional information. PSI has found many applications in the real world, such as private data mining [26], the analysis of genomics or medical data [4,22,33,31] or even botnet detection [24]. Since it was first introduced, PSI has evolved in many subvariants of protocols according to the scenario in which it is applied.…”

Section: Private Set Intersectionmentioning

confidence: 99%

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Azogagh

Delfour

Gambs

et al. 2022

Proceedings of the 10th Workshop on Encrypted Computing &Amp; Applied Homomorphic Cryptography

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Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is particularly challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. Through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private individual graph of each party, even though they know the number of nodes they have, since they share the same graph but not the same links. Crypto'Graph improves on previous works by enabling the computation of a certain number of similarity metrics without any additional cost. The use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which it is possible to identify potential adversarial links without compromising the privacy of the graphs of individual parties. The effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a graph neural network node classification task is demonstrated through extensive experimentation on a real-world dataset.

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Section: Private Set Intersectionmentioning

confidence: 99%

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Azogagh

Delfour

Gambs

et al. 2022

Proceedings of the 10th Workshop on Encrypted Computing &Amp; Applied Homomorphic Cryptography

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“…In (11) , defined the first privacy outsourced method for measuring privacy on data. They proposed the model, in which the database is partitioned horizontally and vertically to create privacy using Private-Set Intersection (PSI).…”

Section: Introductionmentioning

confidence: 99%

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

Mangayarkkarasi

2024

IJST

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Background/Objectives: It is becoming more common for data owners to outsource data mining tasks and storage to cloud service providers as a result of the rising costs of maintaining IT infrastructures for large-scale data mining. This trend, however, also raises security concerns about unauthorized breaches of data confidentiality and outcome integrity. Methods: This research considers this scenario in which cloud user can encrypt their data and store it to cloud environment. In order to perform mining operation, the user needs to outsource the task to cloud servers. Then, the cloud server performs the mining task on the encrypted data and share the encrypted association rule to the cloud user. Yet, existing single cloud server systems have privacy leakage issues since their work focuses on either database privacy or item privacy. To remedy this gap in the literature, this study maintains both database privacy and item privacy during the frequent itemset mining process. For item privacy, it first describes a universal safe multiplication protocol with a single cloud server. We build the inner product rules, comparison rules, frequent itemset protocol, and final association rule mining process that is secure against privacy leaking on top of this multiplication protocol. During this Association Rule Mining (ARM) operation, it provides two level of protection to data privacy. This model is designed with distributed Elgamal cryptosystem and sub-protocols for item and database privacy along with Deep learning-based Support Vector Machine (SVM) for secure rule generation. Findings: The proposed method is named as two-level privacy preserving method association with Deep SVM model (2-level:D-SVM), provides guaranteed solutions to the confidentiality of the outsourced cloud data and minimizes the user interaction during association rule mining task. Here, data on breast cancer and heart disease are used, and the effectiveness of the proposed model is demonstrated by comparison to existing models. According to the study, at 25000 transactions, the proposed 2-level:D-SVM model stands for 52% and 50% more efficient than Parallel Processing (PP) and Privacy-preserving Collaborative Model Learning (PCML) techniques in terms of computing cost. Additionally, the proposed model performs 34%, 22%, 6%, and 4% better in terms of execution time https://www.indjst.org/

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“…Most large hospitals use the same EHR system schema because they follow the same standards for patient information storage in hospitals. As a result, in our study, we have included data that has been horizontally partitioned among the collaborative EHR systems [ 34 – 37 ]. With this insight, we’re working to acquire global association rules while also safeguarding the privacy of EHR systems worldwide.…”

Section: Introductionmentioning

confidence: 99%

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

Domadiya

Rao

2022

J. Inst. Eng. India Ser. B

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In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

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Secure Association Rule Mining on Vertically Partitioned Data Using Private-Set Intersection

Cited by 12 publications

References 34 publications

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Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

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