Wang, Y., Leon, P.G., Acquisti, A., Cranor, L.F., Forget, A., and Sadeh, N.M. A field trial of privacy nudges for facebook. In Proceedings of CHI (2014), 2367--2376.

Digital Library

Google Scholar

[7]

Westin, A. Privacy and Freedom. Altheneum, NY, 1967.

Google Scholar

Cited By

View all

Polan L(2024)Buttery Smooth: Privacy’s Bundling with Attention in Web Browser PerformanceScience, Technology, & Human Values10.1177/01622439241245801Online publication date: 23-Apr-2024
https://doi.org/10.1177/01622439241245801
Akbari Gurabi MHermsen FMandal ADecker S(2024)Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and ChallengesPrivacy and Identity Management. Sharing in a Digital World10.1007/978-3-031-57978-3_11(158-174)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-57978-3_11
Ruotsalainen PBlobel B(2023)Future pHealth Ecosystem-Holistic View on Privacy and TrustJournal of Personalized Medicine10.3390/jpm1307104813:7(1048)Online publication date: 26-Jun-2023
https://doi.org/10.3390/jpm13071048
Show More Cited By

Index Terms

Can you engineer privacy?
1. Security and privacy
  1. Systems security
    1. Operating systems security
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

Recommendations

Can the Utility of Anonymized Data be Used for Privacy Breaches?

Group based anonymization is the most widely studied approach for privacy-preserving data publishing. Privacy models/definitions using group based anonymization includes k-anonymity, l-diversity, and t-closeness, to name a few. The goal of this article ...
Why developers cannot embed privacy into software systems?: An empirical investigation
EASE '18: Proceedings of the 22nd International Conference on Evaluation and Assessment in Software Engineering 2018

Pervasive use of software applications continue to challenge user privacy when users interact with software systems. Even though privacy practices such as Privacy by Design (PbD), have clear instructions for software developers to embed privacy into ...
Translating Privacy Practices into Privacy Promises—How to Promise What You Can Keep
POLICY '03: Proceedings of the 4th IEEE International Workshop on Policies for Distributed Systems and Networks

Enterprises advertise privacy promises using the W3CPlatform for Privacy Preferences (P3P). These privacypromises define what recipients can obtain what collecteddata for what purpose. Internally, enterprises can use fine-grainedprivacy practices such ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Communications of the ACM Volume 57, Issue 8

August 2014

92 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2632661

Editor:
Moshe Y. Vardi
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

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: 01 August 2014

Published in CACM Volume 57, Issue 8

Check for updates

Qualifiers

Column
Popular
Un-reviewed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

40
Total Citations
View Citations
2,917
Total Downloads

Downloads (Last 12 months)353
Downloads (Last 6 weeks)102

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Polan L(2024)Buttery Smooth: Privacy’s Bundling with Attention in Web Browser PerformanceScience, Technology, & Human Values10.1177/01622439241245801Online publication date: 23-Apr-2024
https://doi.org/10.1177/01622439241245801
Akbari Gurabi MHermsen FMandal ADecker S(2024)Towards Privacy-Preserving Machine Learning in Sovereign Data Spaces: Opportunities and ChallengesPrivacy and Identity Management. Sharing in a Digital World10.1007/978-3-031-57978-3_11(158-174)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-3-031-57978-3_11
Ruotsalainen PBlobel B(2023)Future pHealth Ecosystem-Holistic View on Privacy and TrustJournal of Personalized Medicine10.3390/jpm1307104813:7(1048)Online publication date: 26-Jun-2023
https://doi.org/10.3390/jpm13071048
Altaf TWang XNi WLiu RBraun R(2023)NE-GConvComputers and Security10.1016/j.cose.2023.103285130:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103285
Villar-Rodriguez EPérez MTorre-Bastida ASenderos CLópez-de-Armentia J(2023)Edge intelligence secure frameworksComputers and Security10.1016/j.cose.2023.103278130:COnline publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103278
Bhat PBehal SDutta K(2023)A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learningComputers and Security10.1016/j.cose.2023.103277130:COnline publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103277
Ferguson-Walter KMajor MJohnson CJohnson CScott DGutzwiller RShade T(2023)Cyber expert feedbackComputers and Security10.1016/j.cose.2023.103268130:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103268
Dong CYang JLiu SWang ZLiu YLu Z(2023)C-BEDIM and S-BEDIMComputers and Security10.1016/j.cose.2023.103267130:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103267
Guamán DRodriguez Ddel Alamo JSuch J(2023)Automated GDPR compliance assessment for cross-border personal data transfers in android applicationsComputers and Security10.1016/j.cose.2023.103262130:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103262
Lenz JBozakov ZWendzel SVrhovec S(2023)Why people replace their aging smart devicesComputers and Security10.1016/j.cose.2023.103258130:COnline publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1016/j.cose.2023.103258
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Login options

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

Abstract

References

Cited By

Index Terms

Recommendations

Can the Utility of Anonymized Data be Used for Privacy Breaches?

Why developers cannot embed privacy into software systems?: An empirical investigation

Translating Privacy Practices into Privacy Promises—How to Promise What You Can Keep

Comments

Information

Published In

Publisher

Publication History

Check for updates

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Digital Edition

Magazine Site

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations