Protocols for Privacy-Preserving Scalable Record Matching and Ontology Alignment
Sponsor: National Science Foundation
Many application domains, such as intelligence, counter-terrorism, forensics, disease control, often need to cross-match multiple very large datasets, such as watch lists. Because those datasets may contain privacy-sensitive or confidential information, the use of efficient privacy-preserving protocols for cross-matching different datasets is crucial.
A Comprehensive Approach for Data Quality and Provenance in Sensor Networks
Sensor networks enable real-time gathering of large amounts of data that can be mined and analyzed for taking critical actions. As such, sensor networks are a key component of decision-making infrastructures. A critical issue in this context is the trustworthiness of the data being collected.
CAREER: An Integrated Approach For Efficient Privacy Preserving Distributed Data Analytics
Increasingly, different organizations need to securely share their private data to execute many critical tasks. Recently, several different approaches based on secure multi-party computation (SMC) and data sanitization techniques have emerged to enable privacy preserving distributed data analytics. Although SMC based privacy-preserving protocols allow the participating parties to learn only the final (accurate) result, they do not scale well for large amounts of data. On the other hand, sanitization based techniques allow organizations to reveal privacy sensitive data under some privacy guarantees by distorting the data. In many cases, significant data distortion that is needed to preserve privacy could lead to inaccurate results.
CT-T: Collaborative Research: A Semantic Framework for Policy Specification and Enforcement in a Need to Share Environment
We live in the information age, a time when data and knowledge is plentiful and easily moved, processed and mined by machines. This makes it easier to discover knowledge and more efficiently manage our affairs but also increases concerns about information confidentiality, privacy and trust. Balancing these will be a defining challenge in the coming decades and is particularly urgent today in organizations responsible for national defense, law enforcement, emergency services, and public health and safety. The 9/11 Commission addressed this in their report and called for “a paradigm change from Need to Know to Need to Share”. This project will explore one concrete aspect of this shift — how executable policies can help organizations enhance their ability to share information and access while still maintaining appropriate levels of security, confidentiality and privacy.
TWC: Medium: Collaborative Proposal: Policy Compliant Integration of Linked Data
The ubiquity of computing technology and the Internet have created an age of big data that has the potential to greatly enhance the efficiency of our societies and the well-being of all people. The trend comes with problems that threaten to prevent or undermine the benefits. An immediate concern is how to fuse, integrate and analyze data while respecting privacy, security and usage concerns. A second issue is allowing data to remain distributed, enabling its owners to maintain and control quality as well as to enforce security and privacy policies. A final underlying challenge is helping to produce sound and useful results by assuring that systems understand the meaning of the data being integrated and analyzing access and usage policies. For some domains, like health informatics and clinical research, solving these problems will have a significant impact on society.