PRESERVATION
Algorithm 6.1: Distributed Coloring Require: INPUT
6.7 Conclusion
less energy. We note the random coloring scheme cannot find any feasible solu- tion to meet the requirements and does not appear in the diagrams.
In summary, our SPG-based data dissemination protocol combines the advan- tages of two baseline dissemination schemes and can achieve better data privacy and a higher level of data availability while consuming less energy.
for target tracking. We argued that data uncertainty is important to quantify data privacy and data availability, and message content is more important than the number of messages with regard to data uncertainty. As such, we provided a content-based definition of data privacy and data availability, utilizing informa- tion states. To strike a balance between two conflicting objectives, we introduced a graph called the SPG that identifies node pairs whose combined sensed data provide high certainty of the target location, and showed that the task of dissem- inating data to storage nodes is equivalent to the problem of coloring the SPG.
The SPG-based data dissemination protocol consists of the following steps:
(1) constructing the SPG among hot nodes (nodes that detect the target) on demand; (2) coloring the SPG using our energy-efficient distributed coloring algorithm; (3) letting those nodes that provide “valuable” information repli- cate messages with a probability p. The experiment results have shown that the SPG-based data dissemination scheme combines the advantages of two baseline dissemination schemes: the shortest path routing and random coloring protocols.
It can achieve better data privacy and a higher level of data availability while consuming lower energy than either baseline data dissemination scheme.
Bibliography
[1] R. Agrawal and R. Srikant. Privacy-preserving data mining. InProc. of the ACM SIGMOD Conference on Management of Data, 439–450. ACM Press, May 2000.
[2] U. Cetintemel, A. Flinders, and Y. Sun. Power-efficient data dissemination in wireless sensor networks. InProceedings of Workshop on Data Engi- neering for Wireless and Mobile Access (MobiDe), 1–8, 2003.
[3] H. Chan and A. Perrig. Security and privacy in sensor networks.IEEE Com- puter, 36(10):103–105, October 2003.
[4] J. Deng, R. Han, and S. Mishra. Intrusion tolerance and anti-traffic analysis strategies for wireless sensor networks. InProceedings of Conference on Dependable Systems and Networks (DSN), 637, 2004.
[5] C. D´ıaz, S. Seys, J. Claessens, and B. Preneel. Towards measuring anonymity. InProceedings of the 2nd International Conference on Privacy Enhancing Technologies, 54–68, 2003.
[6] M. Erdmann. Randomization for robot tasks: Using dynamic programming in the space of knowledge states.Algorithmica, 10:248–291, October 1993.
[7] S. Ganeriwal, Ram Kumar, and M. B. Srivastava. Timing-sync protocol for sensor networks. In Proceedings of Conference on Embedded Networked Sensor Systems (SenSys), 138–149, 2003.
[8] G. Ganger, P. Khosla, M. Bakkaloglu, M. Bigrigg, G. Goodson, S. Oguz, V. Pandurangan, C. Soules, J. Strunk, and J. Wylie. Survivable storage systems.DARPA Information Survivability Conference and Exposition, 2:
184–195, 2001.
[9] C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scal- able and robust communication paradigm for sensor networks. InProceed- ings of Conference on Mobile Computing and Networks (MobiCOM), 2000.
[10] P. Kamat, Y. Zhang, W. Trappe, and C. Ozturk. Enhancing source-location privacy in sensor network routing. InProceedings of the 25th IEEE Inter- national Conference on Distributed Computing Systems (ICDCS), 2005.
[11] R. Kotla, L. Alvisi, and M. Dahlin. Safestore: A durable and practical stor- age system. InUSENIX Annual Technical Conference, 07–20, 2007.
[12] C. K. Liew, U. J. Choi, and C. J. Liew. A data distortion by probability distribution.ACM Trans. Database Syst., 10(3):395–411, 1985.
[13] N. Linial. Locality in distributed graph algorithms. SIAM J. Computing, 21(1):193–201, 1992.
[14] S. Madden, M. Franklin, J. Hellerstein, and W. Hong. TAG: A tiny aggre- gation service for ad-hoc sensor networks. In Proceedings of the Usenix Symposium on Operating Systems Design and Implementation, 2002.
[15] K. Mehta, D. Liu, and M. Wright. Location privacy in sensor networks against a global eavesdropper. In Proceedings of Conference on Network Protocols (ICNP), 314–323, 2007.
[16] N. Minsky. Intentional resolution of privacy protection in database systems.
Commun. ACM, 19(3):148–159, 1976.
[17] J. M. O’Kane and W. Xu. Energy-efficient target tracking with a sensorless robot and a network of unreliable one-bit proximity sensors. InProc. IEEE International Conference on Robotics and Automation, 2009.
[18] A. Perrig, R. Szewczyk, D. Tygar, V. Wen, and D. Culler. SPINS: Security protocols for sensor networks.Wireless Networks, 8(5):521–534, 2002.
[19] A. Savvides, C. Han, and M. B. Strivastava. Dynamic fine-grained localiza- tion in Ad-Hoc networks of sensors. InInternational Conference on Mobile Computing and Networks (MobiCOM), 166–179, 2001.
[20] A. Serjantov and G. Danezis. Towards an information theoretic metric for anonymity. InProceedings of the 2nd International Conference on Privacy Enhancing Technologies, 41–53, 2003.
[21] M. Shao, S. Zhu, W. Zhang, G. Cao, and Y. Yang. pDCS: Security and pri- vacy support for data-centric sensor networks.IEEE Trans. Mob. Comput., 8(8):1023–1038, 2009.
[22] W. Trappe and L. Washington.Introduction to Cryptography with Coding Theory. Prentice Hall, 2002.
[23] Y. Yang, M. Shao, S. Zhu, B. Urgaonkar, and G. Cao. Towards event source unobservability with minimum network traffic in sensor networks. InPro- ceedings of Conference on Wireless Network Security (WiSec), 77–88, 2008.
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Hiva-Network.Com
Chapter 7
Privacy Preservation for IoT Used in Smart
Buildings
Nico Saputro Ali Ihsan Yurekli Kemal Akkaya Arif Selcuk Uluagac
CONTENTS
7.1 Introduction . . . 136 7.2 Overview of Smart Building Concept . . . 137 7.2.1 Smart building subsystems . . . 138 7.2.2 IoT devices used in smart buildings . . . 140 7.2.3 Intelligence in smart buildings . . . 141 7.3 Privacy Threats in Smart Buildings . . . 144 7.3.1 Privacy of user behavior . . . 145 7.3.2 Location privacy . . . 145 7.3.2.1 Privacy issues with wireless LANs . . . 145 7.3.2.2 RFID privacy issues . . . 147 7.3.3 Visual privacy . . . 147 135
7.4 Privacy-Preserving Approaches in Smart Buildings. . . 147 7.4.1 Wireless LAN privacy-preserving approaches . . . 147 7.4.2 RFID privacy-preserving approaches . . . 149 7.4.3 Video surveillance privacy-preserving approaches . . . 151 7.5 Smart Meter Privacy-Preserving Approaches . . . 153 7.5.1 Anonymization approaches . . . 154 7.5.1.1 Identity pseudonymization . . . 154 7.5.1.2 Anonymity through trusted neighborhood
gateways . . . 155 7.5.2 Power consumption modification approaches . . . 157 7.5.2.1 Load signature moderation . . . 157 7.5.2.2 Power usage data masking . . . 159 7.5.3 Encryption-based approaches . . . 159 7.6 Concluding Remarks and Future Research . . . 160 Bibliography . . . 161