However, differential privacy (DP) provides a natural means of obtaining such guarantees. DP [ 12 , 11 ] provides a statistical definition of privacy and anonymity. It gives strict controls on the risk that an individual can be identified from the result of an algorithm operating on personal data.
You'll get the lates papers with code and state-of-the-art methods. Tip: you can also follow us on Twitter However, differential privacy (DP) provides a natural means of obtaining such guarantees. DP [ 12 , 11 ] provides a statistical definition of privacy and anonymity. It gives strict controls on the risk that an individual can be identified from the result of an algorithm operating on personal data. Many studies have been conducted to improve privacy protection in the transformation phase (e.g., one-way hashing, 77 attribute generalization, 75 n-grams, 70 embedding, 71 cryptography 78). For example, Kho et al. 77 developed a hash-based privacy-protecting record-linkage system and evaluated it across six institutions in Chicago, covering 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
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.
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…
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.
As the PFS 2 algorithm is the first algorithm that supports general FSM under differential privacy, we compare the PFS 2 algorithm with two differentially private sequence database publishing algorithms. The first is the algorithm proposed in which utilizes variable length n-grams (referred to as n-gram). Feb 06, 2018 · We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime. The ubiquitous collection of real-world, fine-grained user mobility data from WiFi access points (APs) has the potential to revolutionize the development and evaluation of mobile network research. However, access to real-world network data is hard to come by; and public releases of network traces without adequate privacy guarantees can reveal users’ visit locations, network usage patterns