Privacy in Statistical Databases [electronic resource] : CENEX-SDC Project International Conference, PSD 2006, Rome, Italy, December 13-15, 2006, Proceedings /
PSD 2006Corporate Author
PSD 2006(2006 :Rome, Italy)
Domingo-Ferrer, Josep.editor.
Franconi, Luisa.editor.
Privacy in statistical databases is a discipline whose purpose is to provide - lutions to the con?ict between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the individuals and enterprises to which statistical data refer. - yond law and ethics, there are also practical reasons for statistical agencies and data collectors to invest in this topic: if individual and corporate respondents feel their privacyguaranteed,they arelikelyto providemoreaccurateresponses. There are at least two traditions in statistical database privacy: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originates from computer science and database technology.Bothstartedinthe1970s,butthe1980sandtheearly1990ssawlittle privacy activity on the computer science side. The Internet era has strengthened the interest of both statisticians and computer scientists in this area. Along with the traditional topics of tabular and microdata protection, some research lines have revived and/or appeared, such as privacy in queryable databases and protocols for private data computation.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Methods for Tabular Protection -- A Method for Preserving Statistical Distributions Subject to Controlled Tabular Adjustment -- Automatic Structure Detection in Constraints of Tabular Data -- A New Approach to Round Tabular Data -- Harmonizing Table Protection: Results of a Study -- Utility and Risk in Tabular Protection -- Effects of Rounding on the Quality and Confidentiality of Statistical Data -- Disclosure Analysis for Two-Way Contingency Tables -- Statistical Disclosure Control Methods Through a Risk-Utility Framework -- A Generalized Negative Binomial Smoothing Model for Sample Disclosure Risk Estimation -- Entry Uniqueness in Margined Tables -- Methods for Microdata Protection -- Combinations of SDC Methods for Microdata Protection -- A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases -- Optimal Multivariate 2-Microaggregation for Microdata Protection: A 2-Approximation -- Using the Jackknife Method to Produce Safe Plots of Microdata -- Combining Blanking and Noise Addition as a Data Disclosure Limitation Method -- Why Swap When You Can Shuffle? A Comparison of the Proximity Swap and Data Shuffle for Numeric Data -- Adjusting Survey Weights When Altering Identifying Design Variables Via Synthetic Data -- Utility and Risk in Microdata Protection -- Risk, Utility and PRAM -- Distance Based Re-identification for Time Series, Analysis of Distances -- Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk -- Using Mahalanobis Distance-Based Record Linkage for Disclosure Risk Assessment -- Improving Individual Risk Estimators -- Protocols for Private Computation -- Single-Database Private Information Retrieval Schemes : Overview, Performance Study, and Usage with Statistical Databases -- Privacy-Preserving Data Set Union -- “Secure” Log-Linear and Logistic Regression Analysis of Distributed Databases -- Case Studies -- Measuring the Impact of Data Protection Techniques on Data Utility: Evidence from the Survey of Consumer Finances -- Protecting the Confidentiality of Survey Tabular Data by Adding Noise to the Underlying Microdata: Application to the Commodity Flow Survey -- Italian Household Expenditure Survey: A Proposal for Data Dissemination -- Software -- The ARGUS Software in CENEX -- Software Development for SDC in R -- On Secure e-Health Systems -- IPUMS-International High Precision Population Census Microdata Samples: Balancing the Privacy-Quality Tradeoff by Means of Restricted Access Extracts.
Privacy in statistical databases is a discipline whose purpose is to provide - lutions to the con?ict between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the individuals and enterprises to which statistical data refer. - yond law and ethics, there are also practical reasons for statistical agencies and data collectors to invest in this topic: if individual and corporate respondents feel their privacyguaranteed,they arelikelyto providemoreaccurateresponses. There are at least two traditions in statistical database privacy: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originates from computer science and database technology.Bothstartedinthe1970s,butthe1980sandtheearly1990ssawlittle privacy activity on the computer science side. The Internet era has strengthened the interest of both statisticians and computer scientists in this area. Along with the traditional topics of tabular and microdata protection, some research lines have revived and/or appeared, such as privacy in queryable databases and protocols for private data computation.
Data encryption (Computer science).
Database management.
Mathematical statistics.
Computers and civilization.
Computers.
Law and legislation.
Artificial intelligence.
Cryptology.
Database Management.
Probability and Statistics in Computer Science.
Computers and Society.
Legal Aspects of Computing.
Artificial Intelligence.

Lecture Notes in Computer Science