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Local Differential Privacy (LDP) is popularly used in practice forprivacy-preserving data collection. Although existing LDP protocols offer highutility for large user populations (100,000 or more users), they perform poorlyin scenarios with small user populations (such as those in the cybersecuritydomain) and lack perturbation mechanisms that are effective for both ordinaland non-ordinal item sequences while protecting sequence length and contentsimultaneously. In this paper, we address the small user population problem byintroducing the concept of Condensed Local Differential Privacy (CLDP) as aspecialization of LDP, and develop a suite of CLDP protocols that offerdesirable statistical utility while preserving privacy. Our protocols supportdifferent types of client data, ranging from ordinal data types in finitemetric spaces (numeric malware infection statistics), to non-ordinal items (OSversions, transaction categories), and to sequences of ordinal and non-ordinalitems. Extensive experiments are conducted on multiple datasets, includingdatasets that are an order of magnitude smaller than those used in existingapproaches, which show that proposed CLDP protocols yield high utility.Furthermore, case studies with Symantec datasets demonstrate that our protocolsaccurately support key cybersecurity-focused tasks of detecting ransomwareoutbreaks, identifying targeted and vulnerable OSs, and inspecting suspiciousactivities on infected machines.

Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy