When the hammer meets the nail: Multi-server PIR for database-driven CRN with location privacy assurance

We show that it is possible to achieve information theoretic location privacy for secondary users (SUs) in database-driven cognitive radio networks (CRNs) with an end-to-end delay less than a second, which is significantly better than that of the existing alternatives offering only a computational privacy. This is achieved based on a keen observation that, by the requirement of Federal Communications Commission (FCC), all certified spectrum databases synchronize their records. Hence, the same copy of spectrum database is available through multiple (distinct) providers. We harness the synergy between multi-server private information retrieval (PIR) and database-driven CRN architecture to offer an optimal level of privacy with high efficiency by exploiting this observation. We demonstrated, analytically and experimentally with deployments on actual cloud systems that, our adaptations of multi-server PIR outperform that of the (currently) fastest single-server PIR by a magnitude of times with information theoretic security, collusion resiliency and fault-tolerance features. Our analysis indicates that multiserver PIR is an ideal cryptographic tool to provide location privacy in database-driven CRNs, in which the requirement of replicated databases is a natural part of the system architecture, and therefore SUs can enjoy all advantages of multi-server PIR without any additional architectural and deployment costs.

[1]  Zhenfu Cao,et al.  Location privacy in database-driven Cognitive Radio Networks: Attacks and countermeasures , 2013, 2013 Proceedings IEEE INFOCOM.

[2]  Mohamed Grissa,et al.  Location Privacy in Cognitive Radio Networks: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[3]  Spiridon Bakiras,et al.  Leveraging P2P interactions for efficient location privacy in database-driven dynamic spectrum access , 2015, Int. J. Netw. Secur..

[4]  Mohamed Grissa,et al.  Cuckoo filter-based location-privacy preservation in database-driven cognitive radio networks , 2015, 2015 World Symposium on Computer Networks and Information Security (WSCNIS).

[5]  Marc-Olivier Killijian,et al.  XPIR : Private Information Retrieval for Everyone , 2016, Proc. Priv. Enhancing Technol..

[6]  Philippe Gaborit,et al.  A fast private information retrieval protocol , 2008, 2008 IEEE International Symposium on Information Theory.

[7]  Ian Goldberg,et al.  Optimally Robust Private Information Retrieval , 2012, USENIX Security Symposium.

[8]  Basavaraj Patil,et al.  Protocol to Access White-Space (PAWS) Databases: Use Cases and Requirements , 2013, RFC.

[9]  Venkatesan Guruswami,et al.  Improved decoding of Reed-Solomon and algebraic-geometric codes , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).

[10]  Yuval Ishai,et al.  Information-Theoretic Private Information Retrieval: A Unified Construction , 2001, ICALP.

[11]  Mehdi Tibouchi,et al.  Cryptanalysis of a (Somewhat) Additively Homomorphic Encryption Scheme Used in PIR , 2015, IACR Cryptol. ePrint Arch..

[12]  Mohamed Grissa,et al.  LPOS: Location Privacy for Optimal Sensing in Cognitive Radio Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[13]  Daniel Smith-Tone,et al.  Report on Post-Quantum Cryptography , 2016 .

[14]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.

[15]  Eyal Kushilevitz,et al.  Private information retrieval , 1998, JACM.

[16]  Akihiro Nakao,et al.  GENI: A federated testbed for innovative network experiments , 2014, Comput. Networks.

[17]  Shuai Li,et al.  Location privacy preservation in collaborative spectrum sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[18]  Spiridon Bakiras,et al.  Efficient Location Privacy for Moving Clients in Database-Driven Dynamic Spectrum Access , 2015, 2015 24th International Conference on Computer Communication and Networks (ICCCN).

[19]  Peng Cheng,et al.  Achieving Bilateral Utility Maximization and Location Privacy Preservation in Database-Driven Cognitive Radio Networks , 2015, 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems.

[20]  Craig Gentry,et al.  Single-Database Private Information Retrieval with Constant Communication Rate , 2005, ICALP.

[21]  Catuscia Palamidessi,et al.  Geo-indistinguishability: differential privacy for location-based systems , 2012, CCS.

[22]  Qian Zhang,et al.  Privacy-Preserving Collaborative Spectrum Sensing With Multiple Service Providers , 2015, IEEE Transactions on Wireless Communications.

[23]  Yi Li,et al.  Optimal strategies for defending location inference attack in database-driven CRNs , 2015, 2015 IEEE International Conference on Communications (ICC).

[24]  Marco Gruteser,et al.  USENIX Association , 1992 .

[25]  Mohamed Grissa,et al.  Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures , 2017, IEEE Transactions on Cognitive Communications and Networking.

[26]  Ian Goldberg,et al.  Improving the Robustness of Private Information Retrieval , 2007 .

[27]  Lei Zhu,et al.  Protocol to Access White-Space (PAWS) Databases , 2015, RFC.

[28]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[29]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[30]  Mohamed Grissa,et al.  An efficient technique for protecting location privacy of cooperative spectrum sensing users , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[31]  Mohamed Grissa,et al.  Preserving the Location Privacy of Secondary Users in Cooperative Spectrum Sensing , 2017, IEEE Transactions on Information Forensics and Security.

[32]  Hui Zang,et al.  Anonymization of location data does not work: a large-scale measurement study , 2011, MobiCom.

[33]  Andy Parrish,et al.  Efficient Computationally Private Information Retrieval from Anonymity or Trapdoor Groups , 2010, ISC.

[34]  Ramachandran Ramjee,et al.  A Critique of FCC'S TV White Space Regulations , 2016, GETMBL.

[35]  Rong Du,et al.  Location Privacy Preserving Dynamic Spectrum Auction in Cognitive Radio Network , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.