PAS: Privacy Algorithms in Systems

Philip S. Yu; Olivera Kotevska; Tyler Derr

doi:10.1145/3511808.3557494

PAS: Privacy Algorithms in Systems

Philip S. Yu, Olivera Kotevska, Tyler Derr

Discrete Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.

Original language	English
Title of host publication	CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	5181-5182
Number of pages	2
ISBN (Electronic)	9781450392365
DOIs	https://doi.org/10.1145/3511808.3557494
State	Published - Oct 17 2022
Event	31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States Duration: Oct 17 2022 → Oct 21 2022

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
ISSN (Print)	2155-0751

Conference

Conference	31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/Territory	United States
City	Atlanta
Period	10/17/22 → 10/21/22

Funding

This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

differential privacy
machine learning
privacy algorithms
systems

Access to Document

10.1145/3511808.3557494

Cite this

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title = "PAS: Privacy Algorithms in Systems",

abstract = "Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.",

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Yu, PS, Kotevska, O & Derr, T 2022, PAS: Privacy Algorithms in Systems. in CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 5181-5182, 31st ACM International Conference on Information and Knowledge Management, CIKM 2022, Atlanta, United States, 10/17/22. https://doi.org/10.1145/3511808.3557494

PAS: Privacy Algorithms in Systems. / Yu, Philip S.; Kotevska, Olivera; Derr, Tyler.
CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2022. p. 5181-5182 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.

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PAS: Privacy Algorithms in Systems

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