Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2486084.2486087acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

Private proximity detection for convex polygons

Published: 23 June 2013 Publication History

Abstract

Proximity detection is an emerging technology in Geo-Social Networks that notifies mobile users when they are in proximity. Nevertheless, users may be unwilling to participate in such applications if they are required to disclose their exact locations to a centralized server and/or their social friends. To this end, private proximity detection protocols allow any two parties to test for proximity while maintaining their locations secret. In particular, a private proximity detection query returns only a boolean result to the querier and, in addition, it guarantees that no party can derive any information regarding the other party's location. However, most of the existing protocols rely on simple grid decompositions of the space and assume that two users are in proximity when they are located inside the same grid cell. In this paper, we extend the notion of private proximity detection, and propose a novel approach that allows a mobile user to define an arbitrary convex polygon on the map and test whether his friends are located therein. Our solution employs a secure two-party computation protocol and is provably secure. We implemented our method on handheld devices and illustrate its efficiency in terms of both computational and communication cost.

References

[1]
M. J. Atallah and W. Du. Secure multi-party computational geometry. In WADS, pages 165--179, 2001.
[2]
D. Boneh, E.-J. Goh, and K. Nissim. Evaluating 2-DNF formulas on ciphertexts. In TCC, pages 325--341, 2005.
[3]
M. de Berg, O. Cheong, M. van Kreveld, and M. Overmars. Computational Geometry: Algorithms and Applications. Springer-Verlag, 3rd edition, 2008.
[4]
T. ElGamal. A public-key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory, 31(4):469--472, 1985.
[5]
Z. Erkin, M. Franz, J. Guajardo, S. Katzenbeisser, I. Lagendijk, and T. Toft. Privacy-preserving face recognition. In PETS, pages 235--253, 2009.
[6]
C. Gentry. Fully homomorphic encryption using ideal lattices. In STOC, pages 169--178, 2009.
[7]
O. Goldreich. Foundations of Cryptography: Volume 1, Basic Tools. Cambridge University Press, 2001.
[8]
Y. Huang, D. Evans, J. Katz, and L. Malka. Faster secure two-party computation using garbled circuits. In USENIX Security Symposium, 2011.
[9]
J. M. Keil. Decomposing a polygon into simpler components. SIAM Journal of Computing, 14(4):799--817, 1985.
[10]
Y. Lindell and B. Pinkas. Secure multiparty computation for privacy-preserving data mining. Journal of Privacy and Confidentiality, 1(1):59--98, 2009.
[11]
H. Lipmaa. Verifiable homomorphic oblivious transfer and private equality test. In ASIACRYPT, pages 416--433, 2003.
[12]
S. Mascetti, C. Bettini, and D. Freni. Longitude: Centralized privacy-preserving computation of users' proximity. In Secure Data Management (SDM), pages 142--157, 2009.
[13]
S. Mascetti, D. Freni, C. Bettini, X. S. Wang, and S. Jajodia. Privacy in geo-social networks: Proximity notification with untrusted service providers and curious buddies. VLDB Journal, 20(4):541--566, 2011.
[14]
M. Naor and B. Pinkas. Computationally secure oblivious transfer. Journal of Cryptology, 18(1):1--35, 2005.
[15]
A. Narayanan, N. Thiagarajan, M. Lakhani, M. Hamburg, and D. Boneh. Location privacy via private proximity testing. In NDSS, 2011.
[16]
P. Paillier. Public-key cryptosystems based on composite degree residuosity classes. In EUROCRYPT, pages 223--238, 1999.
[17]
P. Ruppel, G. Treu, A. Küpper, and C. Linnhoff-Popien. Anonymous user tracking for location-based community services. In LoCA, pages 116--133, 2006.
[18]
L. Siksnys, J. R. Thomsen, S. Saltenis, and M. L. Yiu. Private and flexible proximity detection in mobile social networks. In MDM, pages 75--84, 2010.
[19]
L. Siksnys, J. R. Thomsen, S. Saltenis, M. L. Yiu, and O. Andersen. A location privacy aware friend locator. In SSTD, pages 405--410, 2009.
[20]
T. Thomas. Secure two-party protocols for point inclusion problem. International Journal of Network Security, 9(1):1--7, 2009.
[21]
A. C.-C. Yao. How to generate and exchange secrets. In FOCS, pages 162--167, 1986.
[22]
Y. Ye, L. Huang, W. Yang, and Y. Zhu. Efficient protocols for point-convex hull inclusion decision problems. Journal of Networks, 5(5):559--567, 2010.
[23]
G. Zhong, I. Goldberg, and U. Hengartner. Louis, Lester and Pierre: Three protocols for location privacy. In PETS, pages 62--76, 2007.

Cited By

View all
  • (2023)Privately Evaluating Region Overlaps with Applications to Collaborative Sensor Output Validation2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00063(1013-1029)Online publication date: Jul-2023
  • (2023)Reliable federated learning in a cloud-fog-IoT environmentThe Journal of Supercomputing10.1007/s11227-023-05252-w79:14(15435-15458)Online publication date: 18-Apr-2023
  • (2021)Privacy-Preserving Proximity Detection Framework for Location-Based Services2021 International Conference on Networking and Network Applications (NaNA)10.1109/NaNA53684.2021.00025(99-106)Online publication date: Oct-2021
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
MobiDE '13: Proceedings of the 12th International ACM Workshop on Data Engineering for Wireless and Mobile Acess
June 2013
48 pages
ISBN:9781450321976
DOI:10.1145/2486084
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. location privacy
  2. proximity detection
  3. secure computations

Qualifiers

  • Research-article

Conference

SIGMOD/PODS'13
Sponsor:

Acceptance Rates

Overall Acceptance Rate 23 of 59 submissions, 39%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 26 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2023)Privately Evaluating Region Overlaps with Applications to Collaborative Sensor Output Validation2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00063(1013-1029)Online publication date: Jul-2023
  • (2023)Reliable federated learning in a cloud-fog-IoT environmentThe Journal of Supercomputing10.1007/s11227-023-05252-w79:14(15435-15458)Online publication date: 18-Apr-2023
  • (2021)Privacy-Preserving Proximity Detection Framework for Location-Based Services2021 International Conference on Networking and Network Applications (NaNA)10.1109/NaNA53684.2021.00025(99-106)Online publication date: Oct-2021
  • (2018)Efficient and Privacy-Preserving Dynamic Spatial Query Scheme for Ride-Hailing ServicesIEEE Transactions on Vehicular Technology10.1109/TVT.2018.286886967:11(11084-11097)Online publication date: Nov-2018
  • (2018)Efficient and Privacy-Preserving Proximity Detection Schemes for Social ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2017.27667015:4(2947-2957)Online publication date: Aug-2018
  • (2018)Achieve Efficient and Privacy-Preserving Proximity Detection Scheme for Social ApplicationsSecurity and Privacy in Communication Networks10.1007/978-3-319-78813-5_17(339-355)Online publication date: 11-Apr-2018
  • (2017)From Social Group Utility Maximization to Personalized Location Privacy in Mobile NetworksIEEE/ACM Transactions on Networking (TON)10.1109/TNET.2017.265310225:3(1703-1716)Online publication date: 1-Jun-2017
  • (2017)LoDPD: A Location Difference-Based Proximity Detection Protocol for Fog ComputingIEEE Internet of Things Journal10.1109/JIOT.2017.26705704:5(1117-1124)Online publication date: Oct-2017
  • (2017)Efficient and Privacy-Preserving Polygons Spatial Query Framework for Location-Based ServicesIEEE Internet of Things Journal10.1109/JIOT.2016.25530834:2(536-545)Online publication date: Apr-2017
  • (2017)Security and Privacy in Device-to-Device (D2D) Communication: A ReviewIEEE Communications Surveys & Tutorials10.1109/COMST.2017.264968719:2(1054-1079)Online publication date: Oct-2018
  • Show More Cited By

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media