{'fraction_non_alphanumeric': 0.016, 'fraction_numerical': 0.0, 'mean_word_length': 5.170403587443946, '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}}
Privacy-Preserving Multikeyword Similarity Search Over Outsourced Cloud Data

The amount of data generated by individuals and enterprises is rapidly increasing. With the emerging cloud computing paradigm, the data and corresponding complex management tasks can be outsourced to the cloud for the management flexibility and cost savings. Unfortunately, as the data could be sensitive, the direct data outsourcing would have the problem of privacy leakage. The encryption can be used, before the data outsourcing, with the concern that the operations can still be accomplished by the cloud. We consider the multikeyword similarity search over outsourced cloud data. In particular, with the consideration of the text data only, multiple keywords are specified by the user. The cloud returns the files containing more than a threshold number of input keywords or similar keywords, where the similarity here is defined according to the edit distance metric. We propose three solutions, where blind signature provides the user access privacy, and a novel use of Bloom filter's bit pattern provides the speedup of search task at the cloud side. Our final design to achieve the search is secure against insider threats and efficient in terms of the search time at the cloud side. Performance evaluation and analysis are used to demonstrate the practicality of our proposed solutions.