{'fraction_non_alphanumeric': 0.010739856801909307, 'fraction_numerical': 0.0, 'mean_word_length': 5.169117647058823, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards robustness in query auditing

We consider the online query auditing problem for statistical databases. Given a stream of aggregate queries posed over sensitive data, when should queries be denied in order to protect the privacy of individuals? We construct efficient auditors for max queries and bags of max and min queries in both the partial and full disclosure settings. Our algorithm for the partial disclosure setting involves a novel application of probabilistic inference techniques that may be of independent interest. We also study for the first time, a particular dimension of the utility of an auditing scheme and obtain initial results for the utility of sum auditing when guarding against full disclosure.The result is positive for large databases, indicating that answers to queries will not be riddled with denials.