Differentially private sequential data publication via variable-length n-grams

By Rui Chen, Gergely Ács, Claude Castelluccia. Conference on Computer and Communications Security, 2012

307 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Machine Learning for Synthetic Data Generation: a Review (2023, methodology): “For example, Chen et al. [112] introduces an N-gram-based method to predict the following position based on previous positions for publishing trajectory.”
Study : Privacy Preserving Release of Spatio-temporal Density in Paris (2014, background): “[5] applies DP to location, and more generally, sequential data release.”
Differential Privacy for Deep and Federated Learning: A Survey (2022, background): “The most relevant one is [74] which handles the problem of releasing and guarantying privacy for sequential datasets.”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “Similarly, the branch of differential privacy in perspective of dataset is also divided into two major subcategories: (i) Synopsis of Original Databases and Datasets [70]–[81].”
PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy (2018, unknown): “[22] devise a differentially private algorithm for generating synthetic sequence data, which is subsequently improved in [23], [27].”
Differentially Private Frequent Sequence Mining (2016, unknown): “Two studies have been proposed to address the issue of publishing sequence databases under differential privacy [13], [14].”