Predict: Privacy and Security Enhancing Dynamic Information Collection and Monitoring

In this paper, we present an overview of our ongoing project PREDICT (Privacy and secuRity Enhancing Dynamic Information Collection and moniToring). The overall aim of the project is to develop a framework with algorithms and mechanisms for privacy and security enhanced dynamic data collection, aggregation, and analysis with feedback loops. We discuss each of our research thrusts with research challenges and potential solutions, and report some preliminary results.

[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.