skip to main content
10.1145/3098954.3098968acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaresConference Proceedingsconference-collections
research-article

Constrained PET Composition for Measuring Enforced Privacy

Published: 29 August 2017 Publication History

Abstract

Privacy Enhancing Technologies (PETs) are well-defined, domain-specific means to preserve information privacy in computerized systems, i.e., by protecting Personally Identifiable Information (PII). We believe that increasing privacy awareness and governance will lead to wider adoption of PETs in service infrastructures. To support that, a better understanding of privacy-enhanced services composed out of multiple PETs is necessary. To the best of the authors' knowledge, there is no general domain-independent and formal PET model and research about their composition is missing.
The work at hand presents a formal, set-based and domain-independent taxonomy model for PETs, along with an algebra for constrained composition of PETs. The measurement of enforced privacy in service infrastructures with deployed PETs is one of many use cases for such a PET algebra and is demonstrated subsequently in a scenario with two exemplary privacy-enhanced services.

References

[1]
Alessandro Acquisti, Curtis R Taylor, and Liad Wagman. 2016. The economics of privacy. Available at SSRN 2580411 (2016).
[2]
Michael Backes, Aniket Kate, Praveen Manoharan, Sebastian Meiser, and Esfandiar Mohammadi. 2013. AnoA: A framework for analyzing anonymous communication protocols. In Computer Security Foundations Symposium (CSF), 2013 IEEE 26th. IEEE, 163--178.
[3]
Mario Ballano Barcena, Candid Wueest, and Hon Lau. 2014. How safe is your quantified self. Symantech: Mountain View, CA, USA (2014).
[4]
Vincent Bindschaedler, Reza Shokri, and Carl A Gunter. 2017. Plausible Deniability for Privacy-Preserving Data Synthesis. Proceedings of the VLDB Endowment 10, 5(2017).
[5]
Jens-Matthias Bohli and Andreas Pashalidis. 2011. Relations Among Privacy Notions. ACM Trans. Inf. Syst. Secur. 14, 1, Article 4 (June 2011), 24 pages.
[6]
Jan Camenisch and Els Van Herreweghen. 2002. Design and implementation of the idemix anonymous credential system. In Proceedings of the 9th ACM conference on Computer and communications security. ACM, 21--30.
[7]
David Chaum. 1988. The dining cryptographers problem: Unconditional sender and recipient untraceability. Journal of cryptology 1, 1 (1988), 65--75.
[8]
David L Chaum. 1981. Untraceable electronic mail, return addresses, and digital pseudonyms. Commun. ACM 24, 2 (1981), 84--90.
[9]
Se-Hak Chun. 2015. Privacy Enhancing Technologies (PETs) and Investment Strategies for a Data Market. Procedia-Social and Behavioral Sciences 185 (2015), 271--275.
[10]
Sebastian Clauß and Stefan Schiffner. 2006. Structuring Anonymity Metrics. In Proceedings of the Second ACM Workshop on Digital Identity Management (DIM '06). ACM, New York, NY, USA, 55--62.
[11]
Federal Trade Commission and others. 2007. Fair information practice principles. last modified June 25 (2007).
[12]
J. Daubert, A. Wiesmaier, and P. Kikiras. 2015. A View on Privacy & Trust in IoT. In IEEE ICC IoT/CPS-Security. London, UK.
[13]
Roger Dingledine, Nick Mathewson, and Paul Syverson. 2004. Tor: The Second-generation Onion Router. In Proceedings of the 13th Conference on USENIX Security Symposium - Volume 13 (SSYM'04). USENIX Association, Berkeley, CA, USA, 21--21. http://dl.acm.org/citation.cfm?id=1251375.1251396
[14]
Danny Dolev and Andrew C Yao. 1983. On the security of public key protocols. Information Theory IEEE Transactions on 29, 2 (1983), 198--208.
[15]
Josep Domingo-Ferrer and Vicenç Torra. 2008. A critique of k-anonymity and some of its enhancements. In Availability, Reliability and Security 2008. ARES 08. Third International Conference on. IEEE, 990--993.
[16]
Naipeng Dong, Hugo Jonker, and Jun Pang. 2013. Enforcing privacy in the presence of others: Notions, formalisations and relations. In Computer Security--ESORICS 2013. Springer, 499--516.
[17]
Cynthia Dwork. 2006. Differential privacy. In Automata, languages and programming. Springer, 1--12.
[18]
S. Funke, J. Daubert, A. Wiesmaier, P. Kikiras, and M. Muehlhaeuser. 2015. End-2-End privacy architecture for IoT. In Communications and Network Security (CNS), 2015 IEEE Conference on. 705--706.
[19]
Andy Greenberg. 2016. Apples Differential Privacy is about collecting your Data - but not your Data. https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/. (2016). {07/24/2016}.
[20]
Johannes Heurix, Peter Zimmermann, Thomas Neubauer, and Stefan Fenz. 2015. A taxonomy for privacy enhancing technologies. Computers & Security 53 (2015), 1--17.
[21]
John M Howie. 1995. Fundamentals of semigroup theory. (1995).
[22]
Daniel Kifer and Ashwin Machanavajjhala. 2014. Pufferfish: A framework for mathematical privacy definitions. ACM Transactions on Database Systems (TODS) 39, 1 (2014), 3.
[23]
Dominik Leibenger, Frederik Möllers, Anna Petrlic, Ronald Petrlic, and Christoph Sorge. 2016. Privacy Challenges in the Quantified Self Movement - An EU Perspective. Proceedings on Privacy Enhancing Technologies 2016, 4 (2016). Conference Presentation at PETS 2016.
[24]
McAfee. 2014. Securing the Internet of Things. http://www.mcafee.com/us/resources/solution-briefs/sb-securing-the-iot.pdf. (2014). {06/24/15}.
[25]
Arvind Narayanan and Vitaly Shmatikov. 2008. Robust de-anonymization of large sparse datasets. In Security and Privacy, 2008. SP 2008. IEEE Symposium on. IEEE, 111--125.
[26]
Andreas Pfitzmann and Marit Hansen. 2008. Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management - A Consolidated Proposal for Terminology. (2008).
[27]
Dominik Raub and Rainer Steinwandt. 2006. An algebra for enterprise privacy policies closed under composition and conjunction. In Emerging Trends in Information and Communication Security. Springer, 130--144.
[28]
David Rebollo-Monedero, Javier Parra-Arnau, Claudia Diaz, and Jordi Forné. 2013. On the measurement of privacy as an attackers estimation error. International journal of information security 12, 2 (2013), 129--149.
[29]
Florian Schaub, Rebecca Balebako, Adam L Durity, and Lorrie Faith Cranor. 2015. A design space for effective privacy notices. In Eleventh Symposium On Usable Privacy and Security (SOUPS 2015). 1--17.
[30]
Andrei Serjantov and George Danezis. 2002. Towards an information theoretic metric for anonymity. In Privacy Enhancing Technologies. Springer, 41--53.
[31]
Daniel J Solove. 2008. Understanding privacy. (2008).
[32]
Latanya Sweeney. 2002. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 05 (2002), 557--570.
[33]
Yannis Tzitzikas, Anastasia Analyti, Nicolas Spyratos, and Panos Constantopoulos. 2004. An algebraic approach for specifying compound terms in faceted taxonomies. In Information Modelling and Knowledge Bases XV, 13th European-Japanese Conference on Information Modelling and Knowledge Bases, EJC'03. 67--87.
[34]
G.W. Van Blarkom, J.J. Borking, and J.G.E. Olk. 2003. Handbook of privacy and privacy-enhancing technologies. Privacy Incorporated Software Agent (PISA) Consortium, The Hague (2003).
[35]
Marius Wernke, Pavel Skvortsov, Frank Dürr, and Kurt Rothermel. 2014. A Classification of Location Privacy Attacks and Approaches. Personal Ubiquitous Comput. 18, 1 (Jan. 2014), 163--175.
[36]
Jan Henrik Ziegeldorf, Oscar Garcia Morchon, and Klaus Wehrle. 2014. Privacy in the Internet of Things: threats and challenges. Security and Communication Networks 7, 12 (2014), 2728--2742.
[37]
Christian Zimmermann, Rafael Accorsi, and Günter Müller. 2014. Privacy dashboards: reconciling data-driven business models and privacy. In Availability, Reliability and Security (ARES), 2014 Ninth International Conference on. IEEE, 152--157.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ARES '17: Proceedings of the 12th International Conference on Availability, Reliability and Security
August 2017
853 pages
ISBN:9781450352574
DOI:10.1145/3098954
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. PET algebra
  2. PET composition
  3. PET taxonomy
  4. privacy metric

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ARES '17
ARES '17: International Conference on Availability, Reliability and Security
August 29 - September 1, 2017
Reggio Calabria, Italy

Acceptance Rates

ARES '17 Paper Acceptance Rate 100 of 191 submissions, 52%;
Overall Acceptance Rate 228 of 451 submissions, 51%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 133
    Total Downloads
  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media