| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 6, 2020 |
| Latest Amendment Date: | August 6, 2020 |
| Award Number: | 1954814 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Anna Squicciarini
asquicci@nsf.gov (703)292-5177 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2020 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $1,191,106.00 |
| Total Awarded Amount to Date: | $1,191,106.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
101 University Drive, Sutie B6 Amherst MA US 01002-2385 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
A fundamental part of data science is visual data exploration, which uses visualization and user interaction to facilitate the discovery of new knowledge and actionable insights from data. Visual data exploration is well-supported by current visual analytics technology. However, in many domains, the data being explored includes personal facts about individuals. Access to the data may therefore be limited by privacy policies or regulations. This project will develop novel visual data exploration technology that can support the discovery of new knowledge while at the same time guaranteeing that individuals? privacy is protected. These technologies will be studied in the context of healthcare data, government administrative data, and mobility data. This project will expand the safe and effective exploration of private data, allowing a broader community of data scientists to generate insights from a richer set of data sources, including those previously off-limits due to privacy concerns.
The visual exploration methods developed in this project will provide guarantees in the model of differential privacy, which is emerging as the dominant standard for protecting personal data. Enabling accurate visual exploration of data while offering a guarantee of differential privacy requires novel advances in privacy algorithms, visualization technology, as well as careful evaluation methodology and experiments with human subjects. The fundamental challenges of supporting data visualization under differential privacy stem from the complex interaction between privacy algorithms and visualization techniques. Algorithms for private data release can be better designed if they are customized to visualization tasks. And special visualization methods need to be used with noisy privatized data, including those that communicate uncertainty and are robust to spurious visual artifacts. The proposed research has the potential to transform the use of private data by (i) investigating how current visualization and interaction techniques should be adapted in the presence of noise introduced by differentially private algorithms, (ii) developing new algorithms that o?er better visual accuracy, for both static visualizations and interactive visual exploration, and (iii) providing a benchmark and evaluation standards to accelerate innovation in private visualization. The effectiveness and value of these algorithms will be evaluated empirically by running a series of human-centered evaluations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Project Overview
This project investigated how to effectively visualize data while preserving individual privacy through differential privacy (DP) techniques. As organizations increasingly need to share and analyze sensitive data, there is a growing need for methods that can protect individual privacy while maintaining the utility of data visualizations. Our research developed new approaches for creating privacy-preserving visualizations and interactive analysis tools that help analysts work effectively with privatized data.
Key Outcomes and Findings
Understanding Real-World Implementation Challenges
Through interviews with 18 industry practitioners who have implemented differential privacy, we identified critical challenges and opportunities in real-world deployments. The study revealed five distinct implementation stages and highlighted how visualization tools can help bridge communication gaps between different stakeholders. This work provided practical insights for organizations looking to adopt differential privacy.
Improving Privacy-Preserving Data Visualization
We conducted extensive research on creating effective visualizations with differential privacy, focusing particularly on scatterplots. Through extensive evaluation of differentially private scatterplots with various privacy parameters, we developed guidelines for practitioners on how to balance privacy protection with visual utility. We identified metrics that can help automatically assess visualization quality, making it easier for organizations to implement privacy-preserving visualizations effectively.
Interactive Analysis Tools
We developed and evaluated a novel system called "Measure-Observe-Remeasure" that allows analysts to explore private data interactively while maintaining privacy guarantees. The system helps analysts gradually improve accuracy where needed while managing their "privacy budget." User studies with 14 analysts showed they could effectively use the system to perform analysis tasks while preserving privacy, and that their performance favorably compared with measures of theoretically optimal performance.
Technical Innovations
We advanced the state-of-the-art in differentially private synthetic data generation, developing new algorithms that can better support visualization workflows. These methods provide improved accuracy and computational efficiency compared to previous approaches, making privacy-preserving visualization more practical for real-world applications.
Broader Impacts
This research has made several contributions to society beyond its technical innovations. These include:
- Provided practical guidance for organizations implementing differential privacy, helping them better protect individual privacy while maintaining data utility.
- Advanced understanding of how to communicate uncertainty and privacy concepts through visualization.
- Trained graduate students in both privacy-preserving techniques and visualization methods.
- Published findings in leading venues to disseminate knowledge to the broader research community.
The tools and insights from this project help organizations responsibly share and analyze sensitive data, advancing both privacy protection and data-driven decision making. Our work provides a foundation for future research and development in privacy-preserving data visualization.
Last Modified: 01/20/2025
Modified by: Gerome Miklau
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