A survey on differential privacy for unstructured data content
Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …
generated and shared, and it is a challenge to protect sensitive personal information in …
Differentially private natural language models: Recent advances and future directions
Recent developments in deep learning have led to great success in various natural
language processing (NLP) tasks. However, these applications may involve data that …
language processing (NLP) tasks. However, these applications may involve data that …
Synthetic text generation with differential privacy: A simple and practical recipe
Privacy concerns have attracted increasing attention in data-driven products due to the
tendency of machine learning models to memorize sensitive training data. Generating …
tendency of machine learning models to memorize sensitive training data. Generating …
Sentence-level privacy for document embeddings
User language data can contain highly sensitive personal content. As such, it is imperative
to offer users a strong and interpretable privacy guarantee when learning from their data. In …
to offer users a strong and interpretable privacy guarantee when learning from their data. In …
Scenario-based Adaptations of Differential Privacy: A Technical Survey
Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …
privacy preservation. It has had great success in scenarios of local data privacy and …
Privacy-preserving data integration and sharing in multi-party iot environments: An entity embedding perspective
J Lu, H Leung, N **e - Information Fusion, 2024 - Elsevier
The increasing prevalence of IoT applications highlights the urgency for insightful data
fusion and information acquisition, boosting data integration and sharing needs. However …
fusion and information acquisition, boosting data integration and sharing needs. However …
1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy
The study of privacy-preserving Natural Language Processing (NLP) has gained rising
attention in recent years. One promising avenue studies the integration of Differential …
attention in recent years. One promising avenue studies the integration of Differential …
Just rewrite it again: A post-processing method for enhanced semantic similarity and privacy preservation of differentially private rewritten text
The study of Differential Privacy (DP) in Natural Language Processing often views the task of
text privatization as a rewriting task, in which sensitive input texts are rewritten to hide …
text privatization as a rewriting task, in which sensitive input texts are rewritten to hide …
Private Language Models via Truncated Laplacian Mechanism
Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent
privacy leakage, researchers have investigated word-level perturbations, relying on the …
privacy leakage, researchers have investigated word-level perturbations, relying on the …
A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level
on which a proposed mechanism operates, often taking the form of $\textit {word-level} $ or …
on which a proposed mechanism operates, often taking the form of $\textit {word-level} $ or …