9 citations · 38 across the 15 of their papers we have counts for
5 papers · 1 filter
TEM: High Utility Metric Differential Privacy on Text
Ricardo Silva Carvalho, Theodore Vasiloudis, Oluwaseyi Feyisetan
Ensuring the privacy of users whose data are used to train Natural Language Processing (NLP) models is necessary to build and maintain customer trust. Differential Privacy (DP) has…
BRR: Preserving Privacy of Text Data Efficiently on Device
Ricardo Silva Carvalho, Theodore Vasiloudis, Oluwaseyi Feyisetan
With the use of personal devices connected to the Internet for tasks such as searches and shopping becoming ubiquitous, ensuring the privacy of the users of such services has becom…
Reconstructing Test Labels from Noisy Loss Functions
Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu +2
Machine learning classifiers rely on loss functions for performance evaluation, often on a private (hidden) dataset. In a recent line of research, label inference was introduced as…
Label Inference Attacks from Log-loss Scores
Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu +2
Log-loss (also known as cross-entropy loss) metric is ubiquitously used across machine learning applications to assess the performance of classification algorithms. In this paper,…
On a Utilitarian Approach to Privacy Preserving Text Generation
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan +1
Differentially-private mechanisms for text generation typically add carefully calibrated noise to input words and use the nearest neighbor to the noised input as the output word. W…