poster

Public Access

Towards Practical Privacy-Preserving Life Cycle Assessment Computations

Authors:

Cetin Sahin,

Brandon Kuczenski,

Omer Egecioglu,

Amr El AbbadiAuthors Info & Claims

CODASPY '17: Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy

Pages 167 - 169

https://doi.org/10.1145/3029806.3029845

Published: 22 March 2017 Publication History

PDF eReader

Abstract

Life Cycle Assessment(LCA) is crucial for evaluating the ecological sustainability of a product or service, and the accurate evaluation of sustainability requires detailed and transparent information about industrial activities. However, such information is usually considered confidential and withheld from the public. In this paper, we present a rigorous study of privacy in the context of LCA. The main goal is to explore the privacy challenges in sustainability assessment considering the protection of trade secrets while increasing transparency of industrial activities. To overcome privacy concerns, we apply differential privacy to LCA computations considering the idiosyncratic features of LCA data. Our assessments on a specific real-life example show that it is possible to achieve privacy-preserving LCA computations without losing the utility of data completely.

References

[1]

U.S. Life Cycle Inventory Database, 2012. National Renewable Energy Laboratory, 2012. Accessed March 11, 2016: https://www.lcacommons.gov/nrel/search.

Google Scholar

[2]

C. Dwork. Differential privacy. In ICALP 2006, Proceedings, Part II, pages 1--12. Springer Berlin Heidelberg, 2006.

Digital Library

Google Scholar

[3]

G. Finnveden, M. Z. Hauschild, T. Ekvall, J. Guinée, R. Heijungs, S. Hellweg, A. Koehler, D. Pennington, and S. Suh. Recent developments in life cycle assessment. Journal of Environmental Management, 91(1):1--21, 2009.

Crossref

Google Scholar

[4]

R. Frischknecht. Transparency in LCA-a heretical request? Int J LCA, 9(4):211--213, jul 2004.

Crossref

Google Scholar

[5]

B. Kuczenski. Partial ordering of life cycle inventory databases. The International Journal of Life Cycle Assessment, 20(12):1673--1683, Oct 2015.

Crossref

Google Scholar

[6]

E. H. Moore. On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society, 26:394--395, 1920.

Google Scholar

[7]

C. Sahin, B. Kuczenski, O. Egecioglu, and A. El Abbadi. Towards Practical Privacy-Preserving Life Cycle Assessment Computations. Technical report. January 2017. https://www.cs.ucsb.edu/research/tech-reports/2017-01.

Google Scholar

[8]

A.-M. Tillman. Significance of decision-making for LCA methodology. Environ.\ Impact Assess. Rev., 20(1):113 -- 123, 2000.

Google Scholar

Cited By

View all

Perera HAtmojo UVyatkin V(2024)Confidentiality Preserving Data Sharing for Life Cycle Assessment in Process Industries2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA61755.2024.10710738(1-4)Online publication date: 10-Sep-2024
https://doi.org/10.1109/ETFA61755.2024.10710738
Brucker AYalman S(2022)Confidentiality Enhanced Life-Cycle AssessmentBusiness Process Management Workshops10.1007/978-3-030-94343-1_33(434-446)Online publication date: 23-Jan-2022
https://doi.org/10.1007/978-3-030-94343-1_33

Index Terms

Towards Practical Privacy-Preserving Life Cycle Assessment Computations
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

Recommendations

Privacy-Preserving Certification of Sustainability Metrics
CODASPY '18: Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy

Companies are often motivated to evaluate their environmental sustainability, and to make public pronouncements about their performance with respect to quantitative sustainability metrics. Public trust in these declarations is enhanced if the claims are ...
An Emerging Strategy for Privacy Preserving Databases: Differential Privacy
HCI for Cybersecurity, Privacy and Trust
Abstract
Data De-identification and Differential Privacy are two possible approaches for providing data security and user privacy. Data de-identification is the process where the personal identifiable information of individuals is extracted to create ...
Protecting sensitive place visits in privacy-preserving trajectory publishing
Highlights
- We propose a method for privacy-preserving trajectory publishing.
- It aims at ...
Abstract
The rise of mobile computing has generated huge amount of trajectory data. Since these data are valuable for many people, publishing them while providing adequate individual privacy protection has been a challenging task. In this paper,...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CODASPY '17: Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy

March 2017

382 pages

ISBN:9781450345231

DOI:10.1145/3029806

General Chair:
Gail-Joon Ahn
Arizona State University, USA
,
Program Chairs:
Alexander Pretschner
Technische Universität München, Germany
,
Gabriel Ghinita
University of Massachusetts Boston, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2017

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

National Science Foundation

Conference

CODASPY '17

Sponsor:

SIGSAC

CODASPY '17: Seventh ACM Conference on Data and Application Security and Privacy

March 22 - 24, 2017

Arizona, Scottsdale, USA

Acceptance Rates

CODASPY '17 Paper Acceptance Rate 21 of 134 submissions, 16%;

Overall Acceptance Rate 149 of 789 submissions, 19%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
295
Total Downloads

Downloads (Last 12 months)54
Downloads (Last 6 weeks)13

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Perera HAtmojo UVyatkin V(2024)Confidentiality Preserving Data Sharing for Life Cycle Assessment in Process Industries2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA61755.2024.10710738(1-4)Online publication date: 10-Sep-2024
https://doi.org/10.1109/ETFA61755.2024.10710738
Brucker AYalman S(2022)Confidentiality Enhanced Life-Cycle AssessmentBusiness Process Management Workshops10.1007/978-3-030-94343-1_33(434-446)Online publication date: 23-Jan-2022
https://doi.org/10.1007/978-3-030-94343-1_33

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Privacy-Preserving Certification of Sustainability Metrics

An Emerging Strategy for Privacy Preserving Databases: Differential Privacy

Protecting sensitive place visits in privacy-preserving trajectory publishing