Privacy Disclosure in the Real World

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Network Security ◽  
Real World ◽  
Privacy Protection ◽  
Information Disclosure ◽  
The Real ◽  
Public Figures ◽  
Measurement Algorithm ◽  
Privacy Measurement

Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.

scholarly journals Selebgram: Meraih Popularitas melalui Cyberspace

2018 ◽  
Vol 3 (1) ◽  
pp. 48
Author(s):  
Sakinah - Sakinah
Keyword(s):  
Social Media ◽  
Real World ◽  
Media Communication ◽  
The Real ◽  
Face To Face ◽  
Public Figures ◽  
Search Field ◽  
Audio Information

Media communication began to shift toward cyber media with the presence of various kinds of social media as a form of modern communication which not only can share audio information, but also images and video, including Instagram. Uploaded content can be appreciated by netizens who then create public figures in social media instagram, called Selebgram. This article is focused on how netizens categorize selebgram and what selebgrams’ strategies to become popular and how they maintain their popularity. The study shows that netizens categorize a person as a selebgram not only based on the number of followers, the number of likers and comments on the uploaded content, taking endorsement, but also appear in the search field. Some selebgram use fake account provider to get more followers instantly, others beautifying the uploaded content, channelling hobbies, and into coincidental programming. To maintain their followers, a selebgram uploads content by following and creating trends, uploading periodically to update the content of their timeline and utilizing the offered endorsement. It is also found that interaction between selebgram and their followers is more active in Instagram (i.e. comment and direct message) than face to face interaction. Followers even tend to feel reluctant to directly greet, take picture or ask for signature of the selebgram in the view of the fact that selebgram is a celebrity of Instagram, not celebrity in the real world.

ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION

2002 ◽  
Vol 10 (05) ◽  
pp. 571-588 ◽  
Author(s):  
LATANYA SWEENEY
Keyword(s):  
Real World ◽  
Privacy Protection ◽  
External Information ◽  
The Real ◽  
External Data ◽  
A Value ◽  
Minimal Distortion ◽  
Formal Presentation

Often a data holder, such as a hospital or bank, needs to share person-specific records in such a way that the identities of the individuals who are the subjects of the data cannot be determined. One way to achieve this is to have the released records adhere to k-anonymity, which means each released record has at least (k-1) other records in the release whose values are indistinct over those fields that appear in external data. So, k-anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. Generalization involves replacing (or recoding) a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide k-anonymity protection with minimal distortion. The real-world algorithms Datafly and μ-Argus are compared to MinGen. Both Datafly and μ-Argus use heuristics to make approximations, and so, they do not always yield optimal results. It is shown that Datafly can over distort data and μ-Argus can additionally fail to provide adequate protection.

Anonoymizing Methods against Republication of Incremental Numerical Sensitive Data

2011 ◽  
Vol 267 ◽  
pp. 499-503
Author(s):  
Xiao Lin Zhang ◽  
Jie Yu ◽  
Yue Sheng Tan ◽  
Li Xin Liu
Keyword(s):  
Data Base ◽  
Real World ◽  
Private Information ◽  
Efficient Algorithm ◽  
Privacy Protection ◽  
Information Disclosure ◽  
Direct Application ◽  
Data Sources ◽  
Real World Data ◽  
Sensitive Data

Privacy protection for numerical sensitive data has become a serious concerned in many applications. Current privacy protection for numerical sensitive data base on the static datasets. However, most of the real world data sources are dynamic, and the direct application of the existing static datasets privacy preserving techniques often causes the unexpected private information disclosure. This paper anaylisis various leakage risks of republication of incremental numerical sensitive data on numerical sensitive data, and proposes an efficient algorithm on anonoymizing methods against republication of incremental numerical sensitive data,The experiments show that this method protects privacy adequately.

k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

2002 ◽  
Vol 10 (05) ◽  
pp. 557-570 ◽  
Author(s):  
LATANYA SWEENEY
Keyword(s):  
Real World ◽  
Privacy Protection ◽  
Structured Data ◽  
World Systems ◽  
The Real ◽  
Specific Field ◽  
Private Data ◽  
Privately Held

Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k-anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, μ-Argus and k-Similar provide guarantees of privacy protection.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

The Real-World Fellowship

2006 ◽  
Vol 40 (7) ◽  
pp. 47
Author(s):  
LEE SAVIO BEERS
Keyword(s):  
Real World ◽  
The Real
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