Skip to main content
Log in

The Cloaked-Centroid protocol: location privacy protection for a group of users of location-based services

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Several techniques have been recently proposed to protect user location privacy while accessing location-based services (LBSs). However, applying these techniques to protect location privacy for a group of users would lead to user privacy leakage and query inefficiency. In this paper, we propose a two-phase protocol, we name Cloaked-Centroid, which is designed specifically to protect location privacy for a group of users. We identify location privacy issues for a group of users who may ask an LBS for a meeting place that is closest to the group centroid. Our protocol relies on spatial cloaking, an anonymous veto network and a conference key establishment protocol. In the first phase, member locations are cloaked into a single region based on their privacy profiles, and then, a single query is submitted to an LBS. In the second phase, a special secure multiparty computation extracts the meeting point result from the received answer set. Our protocol is resource aware, taking into account the LBS overhead and the communication cost, i.e., the number of nearest neighbor queries sent to a service provider and the number of returned points of interests. Regarding privacy, Cloaked-Centroid protects the location privacy of each group member from those in the group and from anyone outside the group, including the LBS. Moreover, our protocol provides result-set anonymity, which prevents LBS providers and other possible attackers from learning the meeting place location. Extensive experiments show that the proposed protocol is efficient in terms of computation and communication costs. A security analysis shows the resistance of the protocol against collusion, disruption and background knowledge attacks in a malicious model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. An attacker with a prior knowledge about a user approximate location.

  2. www.rtreeportal.com.

References

  1. Ardagna CA, Cremonini M, De Capitani di Vimercati S et al (2011) An obfuscation-based approach for protecting location privacy. IEEE Trans Dependable Secur Comput (TDSC) 8:13–27

    Article  Google Scholar 

  2. Ashouri-Talouki M, Baraani-Dastjerdi A, Selçuk AA (2012) GLP: a cryptographic approach for group location privacy. Comput Commun 35:1527–1533

    Article  Google Scholar 

  3. Bamba B, Liu L, Pesti P et al (2008) Supporting anonymous location queries in mobile environments with PrivacyGrid. In: Proceedings of world wide web conference (WWW ’08), pp 237–246

  4. Bickson D, Reinman T, Dolev D et al (2009) Peer-to-peer secure multi-party numerical computation facing malicious adversaries. Peer-to-Peer Netw Appl J 3:129–144

    Article  Google Scholar 

  5. Boudot F (2000) Efficient proofs that a committed number lies in an interval. In: Proceedings of advances in cryptology (EUROCRYPT’00), pp 431–444

  6. Boyd C, Mathuria A (2003) Protocols for authentication and key establishment. Springer, Berlin, ISBN 978-3-540-43107-7

  7. Burmester M, Desmedt Y (1994) A secure and efficient conference key distribution system. In: Proceedings of advances in cryptology (EUROCRYPT’94), pp 275–286

  8. Camenisch J, Michels M (1999) Proving in zero-knowledge that a number is the product of two safe primes. In: Proceedings of advances in cryptology (EUROCRYPT’99), LNCS, vol 1592, pp 106–121

  9. Chaum D (1988) The dining cryptographers problem: unconditional sender and recipient untraceability. J Cryptol 1:65–67

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen K, Liu L (2011) Geometric data perturbation for privacy preserving outsourced data mining. Knowl Inf Syst 29:657–695

    Article  Google Scholar 

  11. Chow CY, Mokbel MF, Aref WG (2009) Casper*: query processing for location services without compromising privacy. ACM Trans Database Syst 34:1–48

    Article  Google Scholar 

  12. Chow CY, Mokbel MF, Bao J et al (2011) Query-aware location anonymization for road networks. GeoInformatica 15(3):571–607

    Article  Google Scholar 

  13. Chow CY, Mokbel MF (2007) Enabling private continuous queries for revealed user locations. In: Proceedings of international conference on Advances in spatial and temporal databases (SSTD’07), pp 258–273

  14. Chow CY, Mokbel MF, Liu X (2006) A peer-to-peer spatial cloaking algorithm for anonymous location-based services. In: Proceedings of the ACM symposium on advances in geographic information systems (GIS’06), pp 171–178

  15. Chow CY, Mokbel MF, Liu X (2011) Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments. GeoInformatica 15:351–380

    Article  Google Scholar 

  16. Cramer R, Franklin MK, Schoenmakers B et al (1996) Multi-authority secret-ballot elections with linear work. In: Proceedings of advanced in cryptology (EUROCRYPT’69), pp 72–83

  17. Das K, Bhaduri K, Kargupta H (2010) A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks. Knowl Inf Syst 24:341–367

    Article  Google Scholar 

  18. Dewri R (2011) Location privacy and attacker knowledge: who are we fighting against? In: Proceeding of 7th international ICST conference on security and privacy in communication networks, SecureComm, London, UK

  19. Duckham M, Kulik L (2005) A formal model of obfuscation and negotiation for location privacy. In: Proceedings of international conference on pervasive computing (Pervasive’05), pp 152–170

  20. Gedik B, Liu L (2008) Protecting location privacy with personalized k-anonymity: architecture and algorithms. IEEE Trans Mob Comput TMC 7:1–18

    Article  Google Scholar 

  21. Ghinita G, Kalnis P, Skiadopoulos S (2007) MobiHide: a mobile peer-to-peer system for anonymous location-based queries. In: Proceedings of international symposium on advances in spatial and temporal databases (SSTD’07), pp 221–238

  22. Ghinita G, Kalnis P, Skiadopoulos S (2007) PRIVÉ: anonymous location-based queries in distributed mobile systems. In: Proceedings of international conference on world wide web (WWW’07), pp 371–389

  23. Ghinita G, Kalnis P, Kantarcioglu M et al (2009) A hybrid technique for private location-based queries with database protection. In: Proceedings of international symposium on advances in spatial and temporal databases (SSTD’09). LNCS, vol 5644, pp 98–116

  24. Ghinita G, Kalnis P, Khoshgozaran A et al (2008) Private queries in location based services: Anonymizers are not necessary. In: Proceedings of the ACM international conference on management of data (SIGMOD’08), pp 121–132

  25. Goldreich O, Micali S, Wigderson A (1987) How to play any mental game or a completeness theorem for protocols with honest majority. In: Proceedings of the nineteenth annual ACM conference on theory of computing (STOC’87), pp 218–229

  26. Gruteser M, Grunwald D (2003) Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of MobiSys, pp 31–42

  27. Gruteser M, Schelle G, Jain A et al (2003) Privacy-aware location sensor networks. In: Proceedings of USENIX workshop on hot topics in operating systems (HOTOS’03)

  28. Hao F, Zielinski P (2006) A 2-round anonymous veto protocol. In: Proceedings of the 14th international workshop on security protocols, Cambridge. LNCS, vol 5087, pp 202–211

  29. Hao F, Zielinski P (2009) The power of anonymous veto in public discussion. Trans Comput Sci IV 5430:41–52

    Article  Google Scholar 

  30. Hashem T and Kulik L (2007) Safeguarding location privacy in wireless ad-hoc networks. In: Proceedings of international conference on ubiquitous computing (Ubicomp’07), pp 372–390

  31. Hashem T, Kulik L, Zhang R (2010) Privacy preserving group nearest neighbor queries. In: Proceedings of international conference on extending database technology (EDBT’10), pp 489–500

  32. Hu H, Xu J (2009) Non-exposure location anonymity. In: Proceedings of IEEE international conference on data engineering (ICDE’09), pp 1120–1131

  33. Kalnis P, Ghinita G, Mouratidis K et al (2007) Preventing location-based identity inference in anonymous spatial queries. IEEE Trans Knowl Data Eng (IEEE TKDE) 19:1719–1733

    Article  Google Scholar 

  34. Khoshgozaran A, Shahabi C, Shirani-Mehr H (2011) Location privacy: going beyond K-anonymity, cloaking and anonymizers. Knowl Inf Syst 26:435–465

    Article  Google Scholar 

  35. Khoshgozaran A, Shahabi C (2007) Blind evaluation of nearest neighbor queries using space transformation to preserve location privacy. In: Proceedings of international conference on advances in spatial and temporal databases (SSTD’07), pp 239–257

  36. Kiayias A, Yung M (2003) Non-interactive zero-sharing with applications to private distributed decision making. In: Proceedings of financial cryptography. LNCS, vol 2742, pp 303–320

  37. Langheinrich M (2002) A privacy awareness system for ubiquitous computing environments. In: Proceedings of the 4th international conference on ubiquitous computing (UbiComp’02), pp 237–245

  38. Lee B, Oh J, Yu H et al. (2011) Protecting location privacy using location semantics. In: Proceedings of ACM international conference on knowledge discovery and data mining (KDD’11), pp 1289–1297

  39. Lindell Y, Pinkas B (2002) Privacy preserving data mining. J Cryptol 15(3):177–206

    Article  MathSciNet  MATH  Google Scholar 

  40. Mao W (1998) Guaranteed correct sharing of integer factorization with off-line shareholders. In: Proceedings of public key cryptography (PKC’98), pp 27–42

  41. Menezes AJ, Van Oorschot PC, Vanstone SA (1997) Handbook of applied cryptography. CRC Press, Boca Raton

    MATH  Google Scholar 

  42. Mokbel MF (2008) Privacy-preserving location services. In: Proceedings of IEEE international conference on data engineering (ICDM’08), Pisa, Italy (3-hours tutorial)

  43. Mokbel MF, Chow CY, Aref WG (2006) The new casper: query processing for location services without compromising privacy. In: Proceedings of the 32nd international conference on very large data bases (VLDB’06), pp 763–774

  44. Mokbel MF (2007) Privacy in location-based services: state-of-the-art and research directions. In: IEEE international conference on mobile data management, MDM 2007, Mannheim, Germany (3-hours tutorial)

  45. Olumofin F, Tysowski PK, Goldberg I et al (2010) Achieving efficient query privacy for location based services. In: Proceedings of the 10th international conference on privacy enhancing technologies (PETS’10), pp 93–110

  46. Paillier P, Pointcheval D (1999) Efficient public-key cryptosystems provably secure against active adversaries. In: Advances in cryptology (ASIACRYPT’99), pp 165–179

  47. Papadias D, Tao Y, Mouratidis K et al (2005) Aggregate nearest neighbor queries in spatial databases. ACM Trans Database Syst (TODS) 30:529–576

    Article  Google Scholar 

  48. Peng K, Bao F (2010) Batch range proof for practical small ranges. In: Proceedings of the AFRICACRYPT. LNCS, vol 6055, pp 114–130

  49. Pieprzyk J, Hardjono T, Seberry J (2003) Fundamentals of computer security. Springer, Berlin, ISBN 978-3-540-43101-5

  50. Pohlig S, Hellman M (1978) An improved algorithm for computing logarithms over GF(p) and its cryptographic significance. IEEE Trans Inf Theory 24:106–110

    Article  MathSciNet  MATH  Google Scholar 

  51. Ramakrishnan R, Gehrke J (2009) Database Manag Syst, 3rd edn. WCB/McGraw-Hill, New York

    Google Scholar 

  52. Reed MG, Syverson PF, Goldschlag DM (1998) Anonymous connections and onion routing. IEEE J Sel Areas Commun 16:482–494

    Article  Google Scholar 

  53. Sakuma J, Kobayashi S (2010) Large-scale k-means clustering with user-centric privacy-preservation. Knowl Inf Syst 25:253–279

    Article  Google Scholar 

  54. Schnorr CP (1991) Efficient signature generation by smart cards. J Cryptol 4:161–174

    Article  MathSciNet  MATH  Google Scholar 

  55. Solanas A, Domingo-Ferrer J, Martínez-Ballesté A (2008) Location privacy in location-based services: beyond TTP-based schemes. In: Proceeding of 1st international workshop on privacy in location-based applications (PILBA) within 13th European symposium on research in computer security (ESORICS), pp 12–23

  56. Solanas A, Martínez-Ballesté A (2008) A TTP-free protocol for location privacy in location-based services. Comput Commun 31:1181–1191

    Article  Google Scholar 

  57. Strassman M, Collier C (2004) Case study: the development of the find friends application. In: Schiller JH, Voisard A (eds) Location-based services. Morgan Kaufmann, Los Altos, pp 27–40

    Chapter  Google Scholar 

  58. Tai CH, Yu PS, Yang DN et al (2011) Privacy-preserving social network publication against friendship attacks. In: Proceedings of ACM international conference on knowledge discovery and data mining (KDD’11), pp 1262–1270

  59. Yakut I, Polat H (2012) Privacy-preserving hybrid collaborative filtering on cross distributed data. Knowl Inf Syst 30:405–433. doi:10.1007/s10115-011-0395-3

    Article  Google Scholar 

  60. Yang B, Nakagawa B, Sato I, Sakuma J (2010) Collusion-resistant privacy-preserving data mining. In: Proceedings of the ACM international conference on knowledge discovery and data mining (KDD’10), pp 483–492

  61. Yiu ML, Jensen C, Huang X et al (2008) SpaceTwist: managing the trade-offs among location privacy, query performance, and query accuracy in mobile services. In: Proceedings of IEEE international conference on data engineering (ICDE’08), pp 366–375

  62. Zhong G, Goldberg I, Hengartner U (2007) Louis, lester and pierre: three protocols for location privacy. In: Proceedings of privacy enhancing technologies (PET’07), pp 62–76

  63. Zhong G, Hengartner U (2009) A distributed k-anonymity protocol for location privacy. In: Proceedings of IEEE international conference on pervasive computing and communications (PerCom’09), pp 253–262

  64. Zhou B, Pei J (2011) The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowl Inf Syst 28:47–77. doi:10.1007/s10115-010-0311-2

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the CyberSpace Research Institute of the Islamic Republic of Iran.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maede Ashouri-Talouki.

Appendix: Range proofs for the Cloaked-Centroid protocol

Appendix: Range proofs for the Cloaked-Centroid protocol

To prove \(x_i ,y_i \in \left[ {a,b} \right] \) (location coordinates) in the Cloaked-Centroid protocol, the classical range proof [40] can be applied. In this proof that is based on the zero-knowledge proof of a discrete logarithm [54], the prover encodes her secret to its binary representation and then proves that each digit in this representation is either 0 or 1, using a proof of knowledge of 1 out of 2 discrete logarithms [16]. Adapting the classical range proof to the Cloaked-Centroid protocol proceeds as follows:

Assume the parameters of the range proof are the same as the Cloaked-Centroid protocol.

  1. 1.

    The prover generates \(V=g^{x_i}h^{r}\;\hbox {mod}\,p\) as a commitment to \(x_i\) where \(h\) is the generator of \(G\) and \(r\) is a random integer in \(Z_q\).

  2. 2.

    The prover computes \(V^{{\prime }}=V/g^{a}=g^{x_i -a}h^{r}\;\hbox {mod}\,p\); then, the proof that \(x_i \in \left[ {a,b} \right] \) is reduced to the proof that \(x_i -a\in \left[ {0,b-a} \right] \).

  3. 3.

    Let \(x_i -a=x_0 2^{0}+x_1 2^{1}+\cdots +x_m 2^{m}\) be the binary representation of \(x_i -a\), where \(x_j \in \{0,1\}\) and \(j=0,1,\ldots ,m\) where \(m=32\).

  4. 4.

    The prover chooses \(u_0 ,u_1 ,\ldots ,u_m \in _R Z_q\), and computes \(u=u_0 2^{0}+u_1 2^{1}+\cdots +u_m 2^{m}\;\hbox {mod}\,q\). Then, she computes \(u^{{\prime }}=u-r\) and \(E_i =E\left( {x_j ,u_j} \right) =g^{x_j}h^{u_j}\;\hbox {mod}\,p\) for \(j=0,1,\ldots , m\).

  5. 5.

    The prover sends \(E_j\) and \(u^{{\prime }}\) to the verifier.

  6. 6.

    The verifier checks whether \(V^{{\prime }}h^{u^{{\prime }}}\) is equal to \(\mathop \prod \nolimits _{j=0}^m E_j^{2^{j}}\;\hbox {mod}\,p\).

  7. 7.

    For each \(E_j (j=0,1,\ldots ,m)\), the prover and the verifier run a sub-protocol to prove that the \(x_j\) value is either 0 or 1. This can be done by applying the zero-knowledge proof of knowledge of 1 out of 2 discrete logarithms [16].

Note that before running the range proof protocol, the prover should prove that \(V=g^{x_i}h^{r}\;\hbox {mod}\,p\) and \(w_i =g^{a_i b_i}g^{a_{i-1} a_i}g^{x_i}\;\hbox {mod}\,p\) hides the same secret \(x_i\) by applying a proof of equality of two discrete logarithms [8]. Also, the verification can either be done centrally by a chosen member in the group or distributedly by all members.

The batch range proof of Peng et al. [48] is similar to the classical range proof and can also be applied. In a batch range proof, the prover represents her secret in a base-k system where \(k\) can be any integer greater than 1. Then, the prover proves \(\log _k (b-a)\) instances of the proof that each digit of the base-\(k\) representation of \(x_i -a\) is in \(Z_k\). This is done using a batch proof in which the \(\log _k (b-a)\) instances of proof of knowledge of 1 out of \(k\) are batched into a single proof [48]. Assuming \(k=2\), the batch proof for \(m\) instances of knowledge of 1 out of 2 discrete logarithms is as follows:

figure q

Assuming \(k=2\), adapting the batch range proof to the Cloaked-Centroid protocol proceeds as follows:

  1. 8.

    Steps 1 to 6 are exactly the same as for the classical range proof.

  2. 9.

    The prover and the verifier run a batch proof of knowledge of 1 out of 2 (or 1 out of \(k\)) discrete logarithms to prove that for each \(E_j (j=0,1,\ldots ,m)\), the value of \(x_j \in \{0,1\}\) using the above batch proof.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ashouri-Talouki, M., Baraani-Dastjerdi, A. & Selçuk, A.A. The Cloaked-Centroid protocol: location privacy protection for a group of users of location-based services. Knowl Inf Syst 45, 589–615 (2015). https://doi.org/10.1007/s10115-014-0809-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-014-0809-0

Keywords

Navigation