WITHDRAWN Exploring Statistical Disclosure Limitation Techniques for Agricultural Data Using Robust Metrics for Risk and Utility

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USDA's National Agricultural Statistics Service (NASS) uses the Census of Agriculture (CoA), surveys, and information from other sources to produce official statistics. To minimize disclosure risks and to maintain the analytical validity of the published data, NASS uses a cell suppression approach for its disclosure program. The research to improve statistical disclosure limitation (SDL) program is ongoing at NASS. It is crucial to develop robust evaluation metrics for disclosure risks and data-information loss to identify and select an optimal SDL method that protects agricultural data while preserving their statistical validity. Although measures of utility are easier to define, the literature on assessing disclosure risk is sparse. The contribution of this work is the introduction of robust metrics developed for both risk and utility. Results obtained from different privacy protection approaches applied to the 2017 CoA are compared based on both their utility and risk. In addition, a Pareto front is developed based on these measures to identify an SDL technique for the NASS disclosure program. Results from these analyses and some final remarks are discussed.

Key Words: Privacy evaluation, Disclosure limitation, Pareto front, Utility-risk tradeoff, Robustness