Heterogeneous differential privacy for vertically partitioned databases

Xia, Yang; Zhu, Tianqing; Ding, Xiuli; Jin, Hai; Zou, Deqing

doi:10.1002/cpe.5607

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…Xia et al (Xia et al, 2019) introduced a novel differential privacy mechanism to preserve the privacy in heterogeneous databases. Authors consider the vertical partitioned data for securing and preserving the information among multiple parties.…”

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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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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“…Dwork et al [5] firstly propose the differential privacy to rigorously protect individual privacy. en, many works [16][17][18][19][20] apply it to the static dataset, which will not be updated in the future.…”

Section: Related Workmentioning

confidence: 99%

Differentially Private Web Browsing Trajectory over Infinite Streams

Liu

Guo

Tan

et al. 2021

Security and Communication Networks

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Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

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“…On the other hand, to compete with these limitations, vertically or horizontally partitioning of the data into disjoint parties has been proposed. Therefore, the amount of noise added to the output according to the parallel composition property of differential privacy with ϵ budget remains unchanged, in other words, the amount of noise added becomes low (Blum, Dwork, McSherry, & Nissim, 2005;Dwork et al, 2006;Friedman & Schuster, 2010;Jagannathan, Pillaipakkamnatt, & Wright, 2009;Xia, Zhu, Ding, Jin, & Zou, 2019). However, when data to be analyzed is partitioned, the data analysis process has to deal with the partitioned data subsets, that may increase the computational cost.…”

Section: Introductionmentioning

confidence: 99%

Privacy preserving classification over differentially private data

Zorarpacı

Özel

2020

WIREs Data Min & Knowl

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Privacy preserving data classification is an important research area in data mining field. The goal of a privacy preserving classification algorithm is to protect the sensitive information as much as possible, while providing satisfactory classification accuracy. Differential privacy is a strong privacy guarantee that enables privacy of sensitive data stored in a database by determining the ratio of sensitive information leakage with respect to an ɛ parameter. In this study, our aim is to investigate the classification performance of the state‐of‐the‐art classification algorithms such as C4.5, Naïve Bayes, One Rule, Bayesian Networks, PART, Ripper, K*, IBk, and Random tree for performing privacy preserving classification. To preserve privacy of the data to be classified, we applied input perturbation technique coming from differential privacy, and observed the relationship between the ɛ parameter values and accuracy of the classifiers. To our best knowledge, this article is the first study that analyzes the performances of the well‐known classification algorithms over differentially private data, and discovers which datasets are more suitable for privacy preserving classification when input perturbation is applied to provide data privacy. The classification algorithms are compared by using the differentially private versions of the well‐known datasets from the UCI repository. According to the experimental results, we observed that, as ɛ parameter value increases, better classification accuracies are achieved with lower privacy levels. When the classifiers are compared, Naïve Bayes classifier is the most successful method. The ɛ parameter should be greater than or equal to 2 (i.e., ɛ ≥2) to achieve cloud server is malicious and untrusted, sensitive data will satisfactory classification accuracies. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Classification

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Heterogeneous differential privacy for vertically partitioned databases

Cited by 3 publications

References 33 publications

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

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

Differentially Private Web Browsing Trajectory over Infinite Streams

Privacy preserving classification over differentially private data

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