A survey of personal privacy protection in public service mashups

2011 ◽  
Author(s):  
Rob Smith ◽  
Jie Xu
Keyword(s):  
Public Service ◽  
Privacy Protection ◽  
Personal Privacy

COVID-19 Contact Tracing & Privacy Protection, We Are on the Way (Preprint)

2020 ◽  
Author(s):  
Jing Fan
Keyword(s):  
Real World ◽  
Privacy Protection ◽  
Contact Tracing ◽  
Personal Privacy ◽  
The Way

UNSTRUCTURED Smartphone-based contact tracing is proven to be effective in epidemic containment. To maintain its utilization meanwhile ensure the protection of personal privacy, different countries came up with different practices, new exploratory solutions may come into real-world practice soon as well.

scholarly journals Personal Privacy Protection Framework Based on Hidden Technology for Smartphones

IEEE Access ◽  
2017 ◽  
pp. 1-1 ◽  
Author(s):  
Shuangxi Hong ◽  
Chuanchang Liu ◽  
Bingfei Ren ◽  
Yuze Huang ◽  
Junliang Chen
Keyword(s):  
Privacy Protection ◽  
Personal Privacy

After Snowden – the evolving landscape of privacy and technology

2017 ◽  
Vol 15 (3) ◽  
pp. 328-335 ◽  
Author(s):  
Robin Wilton
Keyword(s):  
Law Enforcement ◽  
Privacy Protection ◽  
Design Methodology ◽  
Special Edition ◽  
Personal Privacy ◽  
Privacy Risk ◽  
Content Type ◽  
The Relationship ◽  
Online Sources ◽  
National Experience

Purpose This paper aims to provide a non-academic perspective on the research reports of the JICES “Post-Snowden” special edition, from the viewpoint of a privacy advocate with an IT background. Design/methodology/approach This paper was written after reviewing the country reports for Japan, New Zealand, PRC and Taiwan, Spain and Sweden, as well as the Introduction paper. The author has also drawn on online sources such as news articles to substantiate his analysis of attitudes to technical privacy protection post-Snowden. Findings Post-Snowden, the general perception of threats to online privacy has shifted from a predominant focus on commercial threats to a recognition that government activities, in the sphere of intelligence and national security, also give rise to significant privacy risk. Snowden’s disclosures have challenged many of our assumptions about effective oversight of interception capabilities. Citizens’ expectations in this regard depend partly on national experience of the relationship between citizen and government, and can evolve rapidly. The tension between legitimate law enforcement access and personal privacy remains challenging to resolve. Originality/value As a “viewpoint” paper, this submission draws heavily on the author’s experience as a privacy and technology subject-matter expert. Although it therefore contains a higher proportion of opinion than the academic papers in this issue, his hope is that it will stimulate debate and further research.

scholarly journals Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2307 ◽  
Author(s):  
Yancheng Shi ◽  
Zhenjiang Zhang ◽  
Han-Chieh Chao ◽  
Bo Shen
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
Data Centers ◽  
Personal Information ◽  
Rapid Development ◽  
Data Availability ◽  
Personal Privacy ◽  
Data Anonymization ◽  
Data Privacy Protection

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

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