Inference control in statistical databases is about finding tradeoffs between the need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises. These papers cover tabular data and microdata protection, and software and user case studies.

Advances in Inference Control in Statistical Databases: An Overview.- Advances in Inference Control in Statistical Databases: An Overview.- Tabular Data Protection.- Cell Suppression: Experience and Theory.- Bounds on Entries in 3-Dimensional Contingency Tables Subject to Given Marginal Totals.- Extending Cell Suppression to Protect Tabular Data against Several Attackers.- Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions.- HiTaS: A Heuristic Approach to Cell Suppression in Hierarchical Tables.- Microdata Protection.- Model Based Disclosure Protection.- Microdata Protection through Noise Addition.- Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique.- Integrating File and Record Level Disclosure Risk Assessment.- Disclosure Risk Assessment in Perturbative Microdata Protection.- LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection.- Post-Masking Optimization of the Tradeoff between Information Loss and Disclosure Risk in Masked Microdata Sets.- Software and User Case Studies.- The CASC Project.- Tools and Strategies to Protect Multiple Tables with the GHQUAR Cell Suppression Engine.- SDC in the 2000 U.S. Decennial Census.- Applications of Statistical Disclosure Control at Statistics Netherlands.- Empirical Evidences on Protecting Population Uniqueness at Idescat.