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 employ-ing a variable-length n-gram model, which extracts the es-sential information of a sequential database in terms of a set of variable-length n-grams.
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 Jul 02, 2020 · Mobile devices furnish users with various services while on the move, but also raise public concerns about trajectory privacy. Unfortunately, traditio… Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving Feb 22, 2020 · In this new setting ensuring privacy is significantly delicate. We prove that any policy which has certain $\textit{contractive}$ properties would result in a differentially private algorithm. We design two new algorithms, one using Laplace noise and other Gaussian noise, as specific instances of policies satisfying the contractive properties. Recently, researchers begin to leverage differential privacy to solve this challenge. Nevertheless, existing mechanisms make an implicit assumption that the trajectories contain a lot of identical prefixes or n-grams, which is not true in many applications. Apr 11, 2019 · The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in numerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable
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 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. raising increasing concerns on individual privacy. In this paper, we study the sequential pattern mining problem under the differential privacy framework whichprovides formal and provable guarantees of privacy. Due to the nature of the differential privacy mecha-nism which perturbs the frequency results with noise, and the high
[26] G. Barthe, B. Köpf, F. Olmedo, and S. Zanella Béguelin, “Probabilistic relational reasoning for differential privacy,” in Proceedings of the 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, ser. POPL ’12.
Differential privacy has becom e a de facto principl e for privacy-preservin g data analysis tasks, and has had many successful applications in spite of the fact that it We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe U of items, possibly of infinite size, and a database D of users. Each user i contributes a subset W⊆U of items. We want an (ϵ,δ)-differentially private algorithm which outputs a subset S of the union of all sets W such that … 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.