Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise
نویسندگان
چکیده
منابع مشابه
Design of Policy-Aware Differentially Private Algorithms
Recent work has proposed a privacy framework, calledBlowfish, that generalizes differential privacy in order togenerate principled relaxations. Blowfish privacy defini-tions take as input an additional parameter called a policygraph, which specifies which properties about individualsshould be hidden from an adversary. An open question isto characterize when Blowfish priv...
متن کاملDifferentially Private Local Electricity Markets
Privacy-preserving electricity markets have a key role in steering customers towards participation in local electricity markets by guarantying to protect their sensitive information. Moreover, these markets make it possible to statically release and share the market outputs for social good. This paper aims to design a market for local energy communities by implementing Differential Privacy (DP)...
متن کاملPrivate Textual Network Using Gsm Architecture
Generally, there are two ways to communicate from a mobile phone. 1) SMS 2) Place a call. Furthermore, communication between mobile phone and normal land phone is quite expensive. The first aim of this research is to target this problem i.e. whenever you want to send a message to your home phone, you can use the proposed solution without adding some valuable coins to your mobile phone bill. The...
متن کاملDifferentially private instance-based noise mechanisms in practice
Differential privacy is a widely used privacy model today, whose privacy guarantees are obtained to the price of a random perturbation of the result. In some situations, basic differentially private mechanisms may add too much noise to reach a reasonable level of privacy. To answer this shortcoming, several works have provided more technically involved mechanisms, using a new paradigm of differ...
متن کاملGenerating Differentially Private Datasets Using GANs
In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data. We include a Gaussian noise layer in the discriminator of a generative adversarial network to make the output and the gradients differentially private with respect to the training data, and then us...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The International FLAIRS Conference Proceedings
سال: 2021
ISSN: 2334-0762
DOI: 10.32473/flairs.v34i1.128463