Differentially Private Data Publishing: Interactive Setting

2017 ◽  
pp. 23-34
Author(s):  
Tianqing Zhu ◽  
Gang Li ◽  
Wanlei Zhou ◽  
Philip S. Yu
Keyword(s):  
Data Publishing ◽  
Private Data

scholarly journals Associated Attribute-Aware Differentially Private Data Publishing via Microaggregation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 79158-79168 ◽  
Author(s):  
Gaoming Yang ◽  
Xinxin Ye ◽  
Xianjin Fang ◽  
Rongshi Wu ◽  
Li Wang
Keyword(s):  
Data Publishing ◽  
Private Data

Differentially Private Data Publishing: Non-interactive Setting

2017 ◽  
pp. 35-48
Author(s):  
Tianqing Zhu ◽  
Gang Li ◽  
Wanlei Zhou ◽  
Philip S. Yu
Keyword(s):  
Data Publishing ◽  
Private Data

Efficient and Secure Outsourcing of Differentially Private Data Publishing with Multiple Evaluators

2020 ◽  
pp. 1-1
Author(s):  
Jin Li ◽  
Heng Ye ◽  
Tong Li ◽  
Wei Wang ◽  
Wenjing Lou ◽  
...  
Keyword(s):  
Data Publishing ◽  
Secure Outsourcing ◽  
Private Data

scholarly journals Using Feature Selection to Improve the Utility of Differentially Private Data Publishing

2014 ◽  
Vol 37 ◽  
pp. 511-516 ◽  
Author(s):  
Yasser Jafer ◽  
Stan Matwin ◽  
Marina Sokolova
Keyword(s):  
Feature Selection ◽  
Data Publishing ◽  
Private Data

scholarly journals Data mining as a tool in privacy-preserving data publishing

2010 ◽  
Vol 45 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Michal Sramka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Sensitive Information ◽  
Private Data ◽  
Privacy Preserving Data Publishing

ABSTRACTMany databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. The concern here is about individuals’ privacy-keeping the sensitive information about individuals private to them. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures. In contrast, data mining can be also used in practice to aid data owners in their decision on how to share and publish their databases. We present and discuss the role and uses of data mining in these scenarios and also briefly discuss other approaches to private data analysis.

Differentially Private Data Publishing: Settings and Mechanisms

2017 ◽  
pp. 17-21
Author(s):  
Tianqing Zhu ◽  
Gang Li ◽  
Wanlei Zhou ◽  
Philip S. Yu
Keyword(s):  
Data Publishing ◽  
Private Data

Differentially private data publishing via optimal univariate microaggregation and record perturbation

2018 ◽  
Vol 153 ◽  
pp. 78-90 ◽  
Author(s):  
Jordi Soria-Comas ◽  
Josep Domingo-Ferrer
Keyword(s):  
Data Publishing ◽  
Private Data

Differentially private data publishing via cross-moment microaggregation

2020 ◽  
Vol 53 ◽  
pp. 269-288 ◽  
Author(s):  
Javier Parra-Arnau ◽  
Josep Domingo-Ferrer ◽  
Jordi Soria-Comas
Keyword(s):  
Data Publishing ◽  
Private Data

scholarly journals A Secure Based Preserving Social Media Data Mangement System

2021 ◽  
Vol 10 (4) ◽  
pp. 210-214
Author(s):  
Geetha V. ◽  
Gomathy C.K. ◽  
Maddu Pavan Manikanta Kiran ◽  
Rajesh, Gandikota
Keyword(s):  
Social Media ◽  
Main Idea ◽  
Data Publishing ◽  
Activity Data ◽  
Online Activity ◽  
Social Media Data ◽  
Private Data ◽  
User Activity ◽  
Real World Datasets ◽  
Media Data

Personalized suggestions are important to help users find relevant information. It often depends on huge collection of user data, especially users’ online activity (e.g., liking/commenting/sharing) on social media, thereto user interests. Publishing such user activity makes inference attacks easy on the users, as private data (e.g., contact details) are often easily gathered from the users’ activity data. during this module, we proposed PrivacyRank, an adjustable and always protecting privacy on social media data publishing framework , which protects users against frequent attacks while giving personal ranking based recommendations. Its main idea is to continuously blur user activity data like user-specified private data is minimized under a given data budget, which matches round the ranking loss suffer from the knowledge blurring process so on preserve the usage of the info for enabling suggestions. a true world evaluation on both synthetic and real-world datasets displays that our model can provide effective and continuous protection against to the info given by the user, while still conserving the usage of the blurred data for private ranking based suggestion. Compared to other approaches, Privacy Rank achieves both better privacy protection and a far better usage altogether the rank based suggestions use cases we tested.

scholarly journals Improved privacy preserving method for periodical SRS publishing

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250457
Author(s):  
Wei Huang ◽  
Tong Yi ◽  
Haibin Zhu ◽  
Wenqian Shang ◽  
Weiguo Lin
Keyword(s):  
Theoretical Analysis ◽  
Heuristic Algorithm ◽  
Spontaneous Reporting ◽  
Adverse Drug Events ◽  
Information Loss ◽  
New Method ◽  
Data Publishing ◽  
Equivalence Group ◽  
Private Data ◽  
Reporting Systems

Spontaneous reporting systems (SRSs) are used to collect adverse drug events (ADEs) for their evaluation and analysis. Periodical SRS data publication gives rise to a problem where sensitive, private data can be discovered through various attacks. The existing SRS data publishing methods are vulnerable to Medicine Discontinuation Attack(MD-attack) and Substantial symptoms-attack(SS-attack). To remedy this problem, an improved periodical SRS data publishing—PPMS(k, θ, ɑ)-bounding is proposed. This new method can recognize MD-attack by ensuring that each equivalence group contains at least k new medicine discontinuation records. The SS-attack can be thwarted using a heuristic algorithm. Theoretical analysis indicates that PPMS(k, θ, ɑ)-bounding can thwart the above-mentioned attacks. The experimental results also demonstrate that PPMS(k, θ, ɑ)-bounding can provide much better protection for privacy than the existing method and the new method dose not increase the information loss. PPMS(k, θ, ɑ)-bounding can improve the privacy, guaranteeing the information usability of the released tables.

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