Using Homomorphic Encryption to Compute Privacy Preserving Data Mining in a Cloud Computing Environment

Hammami, Hamza; Brahmi, Hanen; Brahmi, Imen; Yahia, Sadok Ben

doi:10.1007/978-3-319-65930-5_32

Cited by 9 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The procedure produced a stego file that appeared identical to the cover file, ensuring that the data's presence remained secret. The challenge of maintaining the secrecy of data undergoing data mining in a cloud context is discussed in [33]. The challenge was to ensure that given n sites containing the data being mined for patterns, I would not be able to determine the patterns discovered from the site.…”

Section: Literature Surveymentioning

confidence: 99%

A novel data classification-based scheme for cloud data security using various cryptographic algorithms

Haq

Kumar

2022

IRASE

View full text Add to dashboard Cite

Digital technologies had an effect on people's lives. The majority of these digital devices rely on cloud storage to meet their memory needs. Hundreds of thousands of images, videos, and audio files are being transferred to cloud storage. Thousands of people around the world access these media every second. Unauthorized access to these media must be avoided. One of the weak points for data breaches is the user-end encryption. This paper suggests a strategy for improving cloud data protection by combining the AES and blowfish encryption and decryption algorithms. AES-256 is used as the first layer, followed by blowfish as the second layer, in the hybrid solution. The output of the first layer is input to the second layer and the final result is analyzed. The proposed method also discusses other combined approaches such as AES with other traditional algorithms but the proposed method gives significant results compared to other approaches.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A novel data classification-based scheme for cloud data security using various cryptographic algorithms

Haq

Kumar

2022

IRASE

View full text Add to dashboard Cite

show abstract

“…Compared with the scheme with strong privacy in [60], which had the same privacy as theirs, their scheme is 3 to 5 orders of magnitude higher. Soon after [55], Hammami et al [64] also used homomorphic encryption to achieve privacy preserving data mining in a cloud computing environment. The processing time consumption of their algorithm is superior to the approach proposed in [65] fitting in the same trend and in the same data characteristics.…”

Section: A: Privacy Preserving Association Rule Mining Over Verticallmentioning

confidence: 99%

Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

2019

View full text Add to dashboard Cite

In the last decades, pervasive computing is generating a growing quantity of data. Data mining (DM) technology has become increasingly popular. However, the excessive collection and analysis of data may violate the privacy of individuals and organizations, which raises privacy concern. Therefore, a new research area known as privacy-preserving DM (PPDM) has emerged and attracted the attention of many researchers who are interested in preventing privacy disclosure during DM. In this paper, we provide a comprehensive review of studies on a specific PPDM, known as privacy-preserving association rule mining (PPARM). We present a detailed taxonomy for the existing PPARM algorithms according to multiple dimensions and then conduct a survey of the most relevant PPARM techniques from the literature. Moreover, we survey and elaborate on each type of metrics used to evaluate PPARM algorithms. Finally, we summarize some conclusions and come up with some future directions and challenges. INDEX TERMS Data mining, privacy preserving, association rule mining, association rule hiding, frequent itemsets, privacy metrics, data utility metrics, complexity metrics.

show abstract

“…The database is vertically distributed so that the rows are the same, but the columns are different. Many PPARM schemes [16][17][18][19][20][21][22][23][24][25] have been proposed to find frequent attribute sets within vertically distributed datasets without revealing private data.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Data mining entails the discovery of unexpected but reusable knowledge from large unorganized datasets. Among the many available data-mining algorithms, association rule mining (ARM) is very common. It was developed to aggregate all data into one site and subsequently mine them. In recent years, organizations in different fields have been required to collaborate to create new value. However, data mining among and within organizations has raised privacy and confidentiality concerns. In our scheme, parties cannot share anything other than the number of records, including the candidate itemset. This study focuses on the private-set intersection instead of the scalar product and shows that this intersection enables organizations to execute ARM on vertically partitioned data, allowing flexible information sharing while preserving privacy without increasing communication and computation costs. Furthermore, we focus on the fact that the number of protocol rounds among parties can be reduced and present three use cases in which the proposed scheme works more effectively than the existing schemes.

show abstract

Using Homomorphic Encryption to Compute Privacy Preserving Data Mining in a Cloud Computing Environment

Cited by 9 publications

References 18 publications

A novel data classification-based scheme for cloud data security using various cryptographic algorithms

A novel data classification-based scheme for cloud data security using various cryptographic algorithms

Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

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

Contact Info

Product

Resources

About