Disclosure Limitation Problem and Markov Basis

Author(s):  
Satoshi Aoki ◽  
Hisayuki Hara ◽  
Akimichi Takemura
2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Lars Vilhuber

This issue is the first to appear after a longer intermission. We have replatformed the journal, but we continue the original mission of publishing innovative materials from many disciplines in the areas of privacy, confidentiality, and disclosure limitation. 


2003 ◽  
pp. 94-119 ◽  
Author(s):  
George T. Duncan ◽  
Stephen F. Roehrig

Government agencies collect and disseminate data that bear on the most important issues of public interest. Advances in information technology, particularly the Internet, have multiplied the tension between demands for evermore comprehensive databases and demands for the shelter of privacy. In mediating between these two conflicting demands, agencies must address a host of difficult problems. These include providing access to information while protecting confidentiality, coping with health information databases, and ensuring consistency with international standards. The policies of agencies are determined by what is right for them to do, what works for them, and what they are required to do by law. They must interpret and respect the ethical imperatives of democratic accountability, constitutional empowerment, and individual autonomy. They must keep pace with technological developments by developing effective measures for making information available to a broad range of users. They must both abide by the mandates of legislation and participate in the process of developing new legislation that is responsive to changes that affect their domain. In managing confidentiality and data access functions, agencies have two basic tools: techniques for disclosure limitation through restricted data and administrative procedures through restricted access. The technical procedures for disclosure limitation involve a range of mathematical and statistical tools. The administrative procedures can be implemented through a variety of institutional mechanisms, ranging from privacy advocates, through internal privacy review boards, to a data and access protection commission.


2020 ◽  
Vol 1530 ◽  
pp. 012071
Author(s):  
Maysaa Jalil Mohammed ◽  
Ali Talib Mohammed

2020 ◽  
Vol 18 (7) ◽  
pp. 49-60
Author(s):  
Husein Hadi Abbas ◽  
Huda Qusay Hashim
Keyword(s):  

2019 ◽  
Vol 109 ◽  
pp. 414-420 ◽  
Author(s):  
Raj Chetty ◽  
John N. Friedman

Building on insights from the differential privacy literature, we develop a simple noise-infusion method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on small samples. Although our method does not offer a formal privacy guarantee, it outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by census tract in the Opportunity Atlas. We provide a step-by-step guide and code to implement our approach.


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