Staircase based differential privacy with branching mechanism for location privacy preservation in wireless sensor networks

Abstract The privacy of an event is a critical aspect of safety in wireless sensor networks. Specially, the location privacy of the reporting sensor nodes is essential to preserving the privacy of the event. Protection the privacy of both the event and the node that observes and reports the corresponding event is critical. In this work, we present a differentially private branching framework for guaranteeing the location privacy of a node and subsequently the event. The proposed framework is based on the premise that an event is normally observed by multiple nodes. This leads to a low sensitivity to transmission by a single source node for the transmissions triggered by an event. If an event is reported by small number of nodes, additional fake traffic is required to be generated. Additionally, dummy sources are required to prevent backtracking. The privacy of an event also imposes the constraint that an adversary must not be able to distinguish between real and fake traffic. Results show that the mechanism initially adds small number of dummy sources which increases the number of source nodes. Later the branching mechanism adds large number of virtual nodes in branches emanating from the routing paths rooted in dummy sources. This increases the number of apparent source nodes substantially thereby ensuring location privacy of the source node.

[1]  Wei Jiang,et al.  Traffic Information Publication with Privacy Preservation , 2014, TIST.

[2]  Basel Alomair,et al.  Statistical Framework for Source Anonymity in Sensor Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[3]  Mimoza Durresi,et al.  Routing through Backbone Structures in Sensor Networks , 2005, 11th International Conference on Parallel and Distributed Systems (ICPADS'05).

[4]  L. Zhang,et al.  A novel scheme for protecting receiver's location privacy in wireless sensor networks , 2008, IEEE Transactions on Wireless Communications.

[5]  Donggang Liu,et al.  Protecting Location Privacy in Sensor Networks against a Global Eavesdropper , 2012, IEEE Transactions on Mobile Computing.

[6]  David Chaum,et al.  Untraceable electronic mail, return addresses, and digital pseudonyms , 1981, CACM.

[7]  Shekhar Verma,et al.  Privacy in wireless sensor networks using ring signature , 2014, J. King Saud Univ. Comput. Inf. Sci..

[8]  Wade Trappe,et al.  Enhancing Source-Location Privacy in Sensor Network Routing , 2005, ICDCS.

[9]  Yi Sun,et al.  A source-location privacy protection strategy via pseudo normal distribution-based phantom routing in WSNs , 2015, SAC.

[10]  Shivakant Mishra,et al.  Enhancing Base Station Security in Wireless Sensor Networks , 2003 .

[11]  Bo Sheng,et al.  Privacy-aware routing in sensor networks , 2009, Comput. Networks.

[12]  Catuscia Palamidessi,et al.  Optimal Geo-Indistinguishable Mechanisms for Location Privacy , 2014, CCS.

[13]  Basel Alomair,et al.  Toward a Statistical Framework for Source Anonymity in Sensor Networks , 2013, IEEE Transactions on Mobile Computing.

[14]  Cyrus Shahabi,et al.  A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing , 2014, Proc. VLDB Endow..

[15]  George Theodorakopoulos,et al.  The Same-Origin Attack against Location Privacy , 2015, WPES@CCS.

[16]  Li Xiong,et al.  Protecting Locations with Differential Privacy under Temporal Correlations , 2014, CCS.

[17]  Prabhat Kumar,et al.  Source Location Privacy Using Fake Source and Phantom Routing (FSAPR) Technique in Wireless Sensor Networks , 2015 .

[18]  Fillia Makedon,et al.  Entrapping adversaries for source protection in sensor networks , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[19]  Javier López,et al.  Probabilistic receiver-location privacy protection in wireless sensor networks , 2015, Inf. Sci..

[20]  Pramod Viswanath,et al.  The Optimal Noise-Adding Mechanism in Differential Privacy , 2012, IEEE Transactions on Information Theory.

[21]  Shivakant Mishra,et al.  Decorrelating wireless sensor network traffic to inhibit traffic analysis attacks , 2006, Pervasive Mob. Comput..

[22]  Liang Zhang,et al.  Protecting Receiver-Location Privacy in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[23]  Xiaoyan Hong,et al.  An Identity-Free and On-Demand Routing Scheme against Anonymity Threats in Mobile Ad Hoc Networks , 2007, IEEE Transactions on Mobile Computing.

[24]  Ling Liu,et al.  Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms , 2008, IEEE Transactions on Mobile Computing.

[25]  Xuemin Shen,et al.  An Efficient Privacy-Preserving Scheme against Traffic Analysis Attacks in Network Coding , 2009, IEEE INFOCOM 2009.

[26]  David Sands,et al.  Differential Privacy , 2015, POPL.

[27]  Kobbi Nissim,et al.  Redrawing the boundaries on purchasing data from privacy-sensitive individuals , 2014, ITCS.

[28]  John Krumm,et al.  A survey of computational location privacy , 2009, Personal and Ubiquitous Computing.

[29]  Sencun Zhu,et al.  Towards Statistically Strong Source Anonymity for Sensor Networks , 2008, INFOCOM.

[30]  James B. D. Joshi,et al.  POSTER: Compromising Cloaking-based Location Privacy Preserving Mechanisms with Location Injection Attacks , 2014, CCS.

[31]  Guohong Cao,et al.  Toward Privacy Preserving and Collusion Resistance in a Location Proof Updating System , 2013, IEEE Transactions on Mobile Computing.

[32]  Sencun Zhu,et al.  Towards event source unobservability with minimum network traffic in sensor networks , 2008, WiSec '08.