Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning techniques, next-generation …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

We propose Agora, the first blockchain-based data marketplace that enables multiple
privacy-concerned parties to get compensated for contributing and exchanging data, without …

Split learning is a distributed training framework that allows multiple parties to jointly train a
machine learning model over vertically partitioned data (partitioned by attributes). The idea …

We propose a new family of label randomizers for training regression models under the
constraint of label differential privacy (DP). In particular, we leverage the trade-offs between …

Gaussian process regression (GPR) is a non-parametric model that has been used in many
real-world applications that involve sensitive personal data (eg, healthcare, finance, etc.) …

With the development of IoT technology, personal data are being collected in many places.
These data can be used to create new services, but consideration must be given to the …

We propose a new family of label randomizers for training regression models under the
constraint of label differential privacy (DP). In particular, we leverage the trade-offs between …

Differential privacy is a mathematical framework for privacy-preserving data analysis.
Changing the hyperparameters of a differentially private algorithm allows one to trade off …

The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its
strong privacy guarantees and flexibility. We study its extension to settings with summaries …