Abstract
With the rapidly increasing prevalence of the DDDAS paradigm, privacy and security issues have come to the forefront. In the measurement, feedback, and control phases of dynamic data driven adaptive systems, protecting data integrity (security) and inferred sensitive information (privacy) from inadvertent release or malicious attack is crucial. The PREDICT (Privacy and secuRity Enhancing Dynamic Information Collection and moniToring) project investigates secure dynamic and adaptive techniques for distributed data collection and fusion, sampling and monitoring, and data modeling that preserve privacy and integrity. These approaches deliver provable guarantees of privacy and security while ensuring high fidelity, and complement encryption-based techniques. Application scenarios include health surveillance data release, traffic analysis, situation awareness and monitoring, and fleet tracking.
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References
2009 H1N1 Flu. http://www.cdc.gov/h1n1flu/
Investigation update: Outbreak of shiga toxin-producing E.coli o104 (stec o104:h4) infections associated with travel to germany. http://www.cdc.gov/ecoli/2011/ecolio104/index.html
Report of the August 2010 Multi-Agency Workshop on InfoSymbiotics/DDDAS, The Power of Dynamic Data Driven Applications Systems. Workshop sponsored by: Air Force Office of Scientific Research and National Science Foundation
M.S. Arulampalam, S. Maskell, N. Gordon, A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking. IEEE Trans. Signal Process. 50, 174–188 (2002)
J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, M.B. Srivastava, Participatory sensing, in Workshop on World-Sensor-Web (WSW’06): Mobile Device Centric Sensor Networks and Applications, 2006
B. Cakici, K. Hebing, M. Grünewald, P. Saretok, A. Hulth, Case: a framework for computer supported outbreak detection. BMC Med. Inform. Decis. Mak. 10, 14 (2010)
J. Chilès, P. Delfiner, Geostatistics: Modeling Spatial Uncertainty. Wiley Series in Probability and Statistics (Wiley, New York, 2009)
F. Darema, Dynamic data driven applications systems: a new paradigm for application simulations and measurements, in Computational Science – ICCS 2004. Lecture Notes in Computer Science, vol. 3038 (Springer, Berlin/Heidelberg, 2004), pp. 662–669
F. Darema, InfoSymbioticSystems/DDDAS and Large-Scale Dynamic Data and Large-Scale Big Computing for Smart Systems, in Proceedings of the 2016 Annual ACM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS, Banff, Canada, 2016
W. Du, M.J. Atallah, Secure multi-party computation problems and their applications: a review and open problems, in NSPW’01: Proceedings of the 2001 Workshop on New Security Paradigms, New York (ACM, 2001), pp. 13–22
C. Dwork, Differential privacy, in Automata, Languages and Programming, Pt 2 (Springer, Berlin/Heidelberg, 2006), p. 4052
C. Dwork, Differential privacy: a survey of results, in TAMC ed. by M. Agrawal, D.-Z. Du, Z. Duan, A. Li. Lecture Notes in Computer Science, vol. 4978 (Springer, Berlin, Heidelberg, 2008), pp. 1–19
C. Dwork, A firm foundation for private data analysis. Commun. ACM 54, 86–95 (2011)
C. Dwork, F. McSherry, K. Nissim, A. Smith, Calibrating noise to sensitivity in private data analysis, in 3rd Theory of Cryptography Conference, New York, US, 2006
L. Fan, L. Xiong, An adaptive approach to real-time aggregate monitoring with differential privacy. IEEE Trans. Knowl. Data Eng. 26(9), 2094–2106 (2014)
L. Fan, L. Bonomi,L. Xiong, V. Sunderam, Monitoring web browsing behaviors with differential privacy, in World Wide Web Conference (WWW’14), Seoul, Korea, 2014
L. Fan, L. Xiong, V. Sunderam, Differentially private multi-dimensional time-series release for traffic monitoring, in 27th IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec), Newark, US, 2013
L. Fan, L. Xiong, Real-time aggregate monitoring with differential privacy, in CIKM, Maui, US, 2012, pp. 2169–2173
L. Fan, L. Xiong, V. Sunderam, Fast: differentially private real-time aggregate monitor with filtering and adaptive sampling (demonstration track), in ACM SIGMOD, New York, US, 2013
B.C.M. Fung, K. Wang, R. Chen, P.S. Yu, Privacy-preserving data publishing: a survey on recent developments. ACM Comput. Surv. 42(4), 1–14 (2010)
L. Pournajaf, L. Xiong, D.A. Garcia-Ulloa, V. Sunderam, Participant privacy in mobile crowd sensing task management: a survey of methods and challenges. ACM SIGMOD Rec. 44(4), 23–34 (2015)
L. Pournajaf, L. Xiong, V. Sunderam, S. Goryczka, Spatial task assignment for crowd sensing with cloaked locations, in IEEE 15th International Conference on Mobile Data Management (MDM), Melbourne, Australia, 2014
A. Aved, K. Hua, A general framework for managing and processing live video data with privacy protection. Multimedia Systems 18(2), 123–143 (2012)
Y. Badr, S. Hariri, Y. AlNashif, E. Blasch, Resilient and trustworthy dynamic data-driven application systems (DDDAS) services for crisis management environments, in Proceedings of the International Conference on Computational Science (ICCS), Reykjavik, Iceland, 2015
E. Blasch, Y.B. Al-Nashif, S. Hariri, Static versus dynamic data information fusion analysis using DDDAS for cyber security trust, in Proceedings of the International Conference on Computational Science (ICCS), Cairns, Australia, 2014
S.L. Garfinkel, M.D. Smith, Guest editors’ introduction: data surveillance. IEEE Secur. Privacy 4(6), 15–17 (2006)
O. Goldreich, Foundations of Cryptography: Volume 2, Basic Applications (Cambridge University Press, New York, 2004)
S. Goryczka, L. Xiong, B. Fung, m-privacy for collaborative data publishing, in IEEE Transactions on Data and Knowledge Engineering (TKDE), 26(10), 2520–2533 (2014)
S. Goryczka, L. Xiong, V. Sunderam, Secure multiparty aggregation with differential privacy: a comparative study, in 6th International Workshop on Privacy and Anonymity in the Information Society (PAIS), Genoa, Italy, 2013
Y. Ioannidis, The history of histograms (abridged), in Proceedings of VLDB Conference, Trento, Italy, 2003
R.E. Kalman, A new approach to linear filtering and prediction problems. J. Basic Eng 82(1), 35–45, 1960
J. Kang, K. Shilton, D. Estrin, J. Burke, M. Hansen, Self-surveillance privacy. Iowa Law Rev. 97, 809–847 (2012)
D. Kifer, A. Machanavajjhala, No free lunch in data privacy, in Proceedings of the 2011 International Conference on Management of Data, SIGMOD’11, Athens Greece, 2011
Y. Lindell, B. Pinkas, Secure multiparty computation for privacy-preserving data mining. Cryptology ePrint Archive, Report 2008/197, 2008. http://eprint.iacr.org/
J. Liu, L. Xiong, J. Luo, J.Z. Huang, Privacy preserving distributed dbscan clustering. Trans. Data Privacy 6, 69–85 (2013)
F. McSherry, Privacy integrated queries: an extensible platform for privacy-preserving data analysis, in SIGMOD, Providence, US, 2009
M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, P. Boda, Peir, the personal environmental impact report, as a platform for participatory sensing systems research, in Proceedings of the 7th International Conference on Mobile Systems, Applications, Services, MobiSys, Krakow, Poland, 2009
V. Rastogi, S. Nath, Differentially private aggregation of distributed time-series with transformation and encryption, in SIGMOD, Indianapolis, US, 2010
D. Shepard, A two-dimensional interpolation function for irregularly-spaced data, in Proceedings of the 1968 23rd ACM National Conference, ACM’68, 1968, pp. 517–524
K. Shilton, Four billion little brothers? Privacy, mobile phones, and ubiquitous data collection. Commun. ACM 52, 48–53 (2009)
M.M. Wagner, A.W. Moore, R.M. Aryel (eds.), Elsevier Academic Press. 2011
Y. Xiao, L. Xiong, C. Yuan, Differentially private data release through multidimensional partitioning, in Secure Data Management, at VLDB, Singapore, 2010, pp. 150–168
W. Yih, S. Deshpande, C. Fuller, D. Heisey-Grove, J. Hsu, B. Kruskal, M. Kulldorff, M. Leach, J. Nordin, J. Patton-Levine, E. Puga, E. Sherwood, I. Shui, R. Platt, Evaluating real-time syndromic surveillance signals from ambulatory care data in four states. Public Health Rep. 125(1), 111–120 (2010)
Acknowledgements
This research is supported by the Air Force Office of Scientific Research (AFOSR) DDDAS program under grants FA9550-12-1-0240 and FA9550-17-1-006.
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Xiong, L., Sunderam, V., Fan, L., Goryczka, S., Pournajaf, L. (2018). Privacy and Security Issues in DDDAS Systems. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_27
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