potential privacy concerns, which raises challenges and opportu-nities for privacy-preserving clustering. In this paper, we study the problem of non-interactive clustering in distributed setting under the framework of local differential privacy. We first extend the Bit Vector, a novel anonymization mechanism to

In an embodiment, differential privacy engine 228 can check the blacklist storage 205 before processing a word (e.g. generating differentially private n-grams). In an embodiment, differential privacy engine (DPE) 228 of a client device 110 sends a word to term learning server 130 only once. both privacy and utility. First, the universe of all grams with a small nvalue is relatively small (note that our approach does not even require to explore the entire universe of all n-grams), and thus we can employ the stronger "-di erential privacy model. Second, the counts of shorter grams are often large enough to resist noise. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. Local differential privacy (LDP) has been established as a strong privacy standard for collecting sensitive information from users. Currently, the best known solution for LDP-compliant frequent term discovery transforms the problem into collecting n-grams under LDP, and subsequently reconstructs terms from the collected n-grams by modelling the

N-grams in the ordered histogram for the position having a frequency below the noise floor can be discarded and excluded from further processing. In an embodiment, if there are “n” samples of n-gram data in the histogram, then the noise floor= c * n ɛ, where ε is a differential privacy constant, and c is a constant.

Naïve Private FSM ID 100 200 300 400 500 Record a c d b c d a b c e d d b a dc Database D Seq unc {a}{b}{c}{d}p. 3 3 4 4 {e} 1C 1: cand 1-seqs noise 0.2-0.4 0.4-0.5 0.8 Sequence {a }{a c}{a d}{c a}

To better suit differential privacy, we propose the use of a novel variable-length n-gram model, which balances the trade-off between information of the underlying database retained and the magnitude of Laplace noise added. The variable-length n-gram model intrinsically fits differential privacy in the sense that it retains the essential

optimizations with differential privacy (see e.g. [11]). Some portions of the framework for jointly estimating position bias and training a ranking function [13] (e.g. using gradient boosted decision trees as a ranker) fit nicely into such a framework; other aspects (e.g. en-forcing k-anonymity thresholds on query and document n-grams) N-grams in the ordered histogram for the position having a frequency below the noise floor can be discarded and excluded from further processing. In an embodiment, if there are “n” samples of n-gram data in the histogram, then the noise floor= c * n ɛ, where ε is a differential privacy constant, and c is a constant. To better suit differential privacy, we propose the use of a novel variable-length n-gram model, which balances the trade-off between information of the underlying database retained and the magnitude of Laplace noise added. The variable-length n-gram model intrinsically fits differential privacy in the sense that it retains the essential Due to the inherent sequentiality and high-dimensionality, it is challenging to apply differential privacy to sequential data. In this paper, we address this challenge by employing a variable-length n-gram model, which extracts the essential information of a sequential database in terms of a set of variable-length n-grams.