Predict: Privacy and Security Enhancing Dynamic Information Collection and Monitoring
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Vaidy S. Sunderam | Li Xiong | Liyue Fan | Slawomir Goryczka | Layla Pournajaf | V. Sunderam | Li Xiong | Slawomir Goryczka | Liyue Fan | Layla Pournajaf
[1] Marianne Winslett,et al. Differentially private data cubes: optimizing noise sources and consistency , 2011, SIGMOD '11.
[2] Anette Hulth,et al. CASE: a framework for computer supported outbreak detection , 2010, BMC Medical Informatics Decis. Mak..
[3] Frederica Darema,et al. Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements , 2004, International Conference on Computational Science.
[4] Pierre,et al. [Wiley Series in Probability and Statistics] Geostatistics (Modeling Spatial Uncertainty) || References , 2012 .
[5] A. Milde-Busch,et al. Results of surveillance for infections with Shiga toxin-producing Escherichia coli (STEC) of serotype O104:H4 after the large outbreak in Germany, July to December 2011. , 2014, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.
[6] Oded Goldreich,et al. The Foundations of Cryptography - Volume 2: Basic Applications , 2001 .
[7] Jian Pei,et al. Data Mining: Concepts and Techniques, 3rd edition , 2006 .
[8] Jean-Paul Chilès,et al. Wiley Series in Probability and Statistics , 2012 .
[9] Cynthia Dwork,et al. Differential Privacy , 2006, ICALP.
[10] Li Xiong,et al. Real-time aggregate monitoring with differential privacy , 2012, CIKM.
[11] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[12] Investigation Update: Outbreak of Shiga Toxin‐Producing E. coli O104 (STEC O104:H4) Infections Associated With Travel to Germany , 2011, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.
[13] Li Xiong,et al. Secure Distributed Data Anonymization and Integration with m-Privacy , 2013 .
[14] Elaine Shi,et al. Private and Continual Release of Statistics , 2010, TSEC.
[15] M. Hansen,et al. Participatory Sensing , 2019, Internet of Things.
[16] Lee Sael,et al. Procedia Computer Science , 2015 .
[17] Deborah Estrin,et al. Self-Surveillance Privacy , 2010 .
[18] Chun Yuan,et al. Differentially Private Data Release through Multidimensional Partitioning , 2010, Secure Data Management.
[19] B. Hamber. Publications , 1998, Weed Technology.
[20] Michael M. Wagner,et al. Handbook of biosurveillance , 2006 .
[21] Yannis E. Ioannidis,et al. The History of Histograms (abridged) , 2003, VLDB.
[22] Cynthia Dwork,et al. Differential Privacy: A Survey of Results , 2008, TAMC.
[23] P Ping Chen,et al. Secure multiparty computation for privacy preserving data mining , 2012 .
[24] Benjamin C. M. Fung,et al. m-Privacy for collaborative data publishing , 2011, 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).
[25] Vaidy S. Sunderam,et al. Secure multiparty aggregation with differential privacy: a comparative study , 2013, EDBT '13.
[26] Jiawei Han,et al. Data Mining: Concepts and Techniques, Second Edition , 2006, The Morgan Kaufmann series in data management systems.
[27] Petra Perner,et al. Data Mining - Concepts and Techniques , 2002, Künstliche Intell..
[28] Deborah Estrin,et al. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.
[29] D. Shepard. A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.
[30] Vaidy S. Sunderam,et al. FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling , 2013, SIGMOD '13.
[31] Suman Nath,et al. Differentially private aggregation of distributed time-series with transformation and encryption , 2010, SIGMOD Conference.
[32] J. Chilès,et al. Geostatistics: Modeling Spatial Uncertainty , 1999 .
[33] Wenliang Du,et al. Secure multi-party computation problems and their applications: a review and open problems , 2001, NSPW '01.
[34] Michael D. Smith,et al. Guest Editors' Introduction: Data Surveillance , 2006, IEEE Security & Privacy Magazine.
[35] Vaidy S. Sunderam,et al. Differentially Private Multi-dimensional Time Series Release for Traffic Monitoring , 2013, DBSec.
[36] Ashwin Machanavajjhala,et al. No free lunch in data privacy , 2011, SIGMOD '11.
[37] Torsten Suel,et al. On Rectangular Partitionings in Two Dimensions: Algorithms, Complexity, and Applications , 1999, ICDT.
[38] W Katherine Yih,et al. Evaluating Real-Time Syndromic Surveillance Signals from Ambulatory Care Data in Four States , 2010, Public health reports.
[39] Moni Naor,et al. Differential privacy under continual observation , 2010, STOC '10.
[40] Philip S. Yu,et al. Privacy-preserving data publishing: A survey of recent developments , 2010, CSUR.
[41] Katie Shilton,et al. Four billion little brothers? , 2009, Commun. ACM.
[42] Joshua Zhexue Huang,et al. Privacy preserving distributed DBSCAN clustering , 2012, EDBT-ICDT '12.
[43] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[44] Oded Goldreich,et al. Foundations of Cryptography: Volume 2, Basic Applications , 2004 .
[45] Frank McSherry,et al. Privacy integrated queries: an extensible platform for privacy-preserving data analysis , 2009, SIGMOD Conference.
[46] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[47] Li Xiong,et al. Adaptively Sharing Time-Series with Differential Privacy , 2012, ArXiv.