PILLAR: How to make semi-private learning more effective
In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public
unlabelled and private labelled data. We propose PILLAR, an easy-to-implement and …
unlabelled and private labelled data. We propose PILLAR, an easy-to-implement and …
Models matter: Setting accurate privacy expectations for local and central differential privacy
Differential privacy is a popular privacy-enhancing technology that has been deployed both
in industry and government agencies. Unfortunately, existing explanations of differential …
in industry and government agencies. Unfortunately, existing explanations of differential …
" I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy
Differential privacy (DP) is a promising framework for privacy-preserving data science, but
recent studies have exposed challenges in bringing this theoretical framework for privacy …
recent studies have exposed challenges in bringing this theoretical framework for privacy …
What to Consider When Considering Differential Privacy for Policy
Differential privacy (DP) is a mathematical definition of privacy that can be widely applied
when publishing data. DP has been recognized as a potential means of adhering to various …
when publishing data. DP has been recognized as a potential means of adhering to various …
Attack-aware noise calibration for differential privacy
Differential privacy (DP) is a widely used approach for mitigating privacy risks when training
machine learning models on sensitive data. DP mechanisms add noise during training to …
machine learning models on sensitive data. DP mechanisms add noise during training to …
Advancing differential privacy: Where we are now and future directions for real-world deployment
In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …
Illuminating the Landscape of Differential Privacy: An Interview Study on the Use of Visualization in Real-World Deployments
As Differential Privacy (DP) transitions from theory to practice, visualization has surfaced as
a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to …
a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to …
A Qualitative Analysis of Practical De-Identification Guides
De-identifying microdata is necessary yet difficult. Myriad techniques exist, which reduce risk
and preserve utility to varying, often unclear extents. We conducted a thematic analysis of 38 …
and preserve utility to varying, often unclear extents. We conducted a thematic analysis of 38 …
Capacity Planning Under Local Differential Privacy With Optimized Budget Selection
S Seyedkazemi, ME Gursoy… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
With the growing popularity of local differential privacy (LDP), there is increasing interest in
its deployment in industrial applications, smart homes, and smart cities. However, the main …
its deployment in industrial applications, smart homes, and smart cities. However, the main …
User-Centric Textual Descriptions of Privacy-Enhancing Technologies for Ad Tracking and Analytics
Describing Privacy Enhancing Technologies (PETs) to the general public is challenging but
essential to convey the privacy protections they provide. Existing research has explored the …
essential to convey the privacy protections they provide. Existing research has explored the …