This position paper observes how different technical and normative conceptions of privacy
have evolved in parallel and describes the practical challenges that these divergent …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

This paper studies how a system operator and a set of agents securely execute a distributed
projected gradient-based algorithm. In particular, each participant holds a set of problem …

Association rule hiding is the process of transforming a transaction database into a sanitized
version to protect sensitive knowledge and patterns. The challenge is to minimize the side …

The decentralized digital currency Bitcoin presents an anonymous alternative to the
centralized banking system and indeed enjoys widespread and increasing adoption. Recent …

In the big data era, the generation of data presents some new characteristics, including wide
distribution, high velocity, high dimensionality, and privacy concern. To address these …

In big data era, the collected data usually contains rich information and hidden knowledge.
Utility-oriented pattern mining and analytics have shown a powerful ability to explore these …

Being able to identify records that correspond to the same entity across diverse databases is
an increasingly important step in many data analytics projects. Research into privacy …

Contemporary biomedical databases include a wide range of information types from various
observational and instrumental sources. Among the most important features that unite …

In this paper, we design, develop, and evaluate a novel information sharing (iShare)
framework for cybersecurity with the goal of protecting confidential information and …