'Synthetic data' diaporamas de présentation

Anonymizing User Location and Profile Information for Privacy-aware Mobile Services

Anonymizing User Location and Profile Information for Privacy-aware Mobile Services. Masanori Mano , Yoshiharu Ishikawa Nagoya University. Outline. Background & Motivation Related Work System Framework Matching Degree Algorithm Experimental Evaluation Conclusions and Future work.

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Multiplicative Weights Algorithms

Multiplicative Weights Algorithms. CompSci 590.03 Instructor: Ashwin Machanavajjhala. This Class. A Simple Multiplicative Weights Algorithm Multiplicative Weights for privately publishing synthetic data. Multiple Experts Problem. Will it rain today? . Yes. Yes. Yes. No.

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Voronoi-based Nearest Neighbor Search for Multi-Dimensional Uncertain Databases

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Non-Public Data in the California Census Research Data Center

Non-Public Data in the California Census Research Data Center . The Basics. What is an RDC? What data are available in the RDC? What kinds of research can be done with RDC resources? What is the process for getting access to RDC data?. CCRDC. California Census Research Data Center

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Privacy Preserving Data Mining: An Overview and Examination of Euclidean Distance Preserving Data Transformation

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Problem description and pipeline

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Ruohua Shi

CVPR 2019 oral. Ruohua Shi. 2019/6/27. Motivation. abundant labeled synthetic data (source domain) + insufficient labeled real data (target domain) = a better performance on real data. domain gap. Motivation. abundant labeled synthetic data (source domain) +

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Keeping Data Confidential in an Era of No Privacy

Keeping Data Confidential in an Era of No Privacy. Prof. Jerry Reiter Department of Statistical Science Duke University. Disclosure limitation setting. Agency seeks to release data on individuals Risk of re-identifications from matching to external databases

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Facial Action Units (AU)

Selective Transfer Machine for Personalized Facial Action Unit Detection Wen-Sheng Chu , Fernando De la Torre and Jeffery F. Cohn Robotics Institute, Carnegie Mellon University July 9, 2013. Facial Action Units (AU). AU 6+12. Main Idea. Related Work: Features. Related Work: Classifiers.

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Team

CSTEP Cluster Sampling for Tail Estimation of Probability. Team. Project Team and Faculty. Created by: Alan Chandler Nathan Wood Eric Brown Temourshah Ahmady Faculty Advisor : Dr. James Schwing Client: Dr. Yvonne Chueh. Introduction.

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Group Linkage

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Continuous Fragmented Skylines over Distributed Streams

Continuous Fragmented Skylines over Distributed Streams. Odysseas Papapetrou and Minos Garofalakis SoftNet laboratory, Technical University of Crete. New requirements for skylines. Distributed and P2P algorithms, tracking of skylines, etc.

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Group 4: Web based applications/ crowdsourcing

Group 4: Web based applications/ crowdsourcing. Marcel Prastawa Ziv Yaniv Patrick Reynolds Stephen Aylward Sean Megason. A2D2s. SCORE: Systematic Comparison through Objective Rating and Evaluation ( Prastawa ):

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Emerging Flux Simulations & proto Active Regions

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Protein docking with PCS restraints

Protein docking with PCS restraints. Christophe Schmitz 29 September 2010. Paramagnetic restraints in Haddock: P seudo c ontact s hift (PCS). PCS (N). PCS (H). PCS for docking proteins. Atom coordinate in the protein frame. ??-tensor parameters. PCS(A34)= 1.2 PCS(H36)=-2.5

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Sander Scholtus

A generalised Fellegi-Holt paradigm for automatic editing. Sander Scholtus. Introduction. Automatic editing as a partial alternative to manual editing: advantages in efficiency timeliness reproducibility of results Methods: deductive editing for systematic errors ( if-then rules)

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Convergence of PageRank and HITS Algorithms

Convergence of PageRank and HITS Algorithms. Victor Boyarshinov Eric Anderson 12/5/02. Outline. Algorithms Convergence Graph data and a bad graph Results. PageRank Algorithm. initialize ranks R 0 while (not converged) for each vertex i end end. HITS Algorithm.

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Nonparametric Latent Feature Models for Link Prediction

Nonparametric Latent Feature Models for Link Prediction. Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan NIPS 2009 Presented by Minhua Chen, 06.04.2010. Problem Formulation. Link prediction in Social Network (Binary Matrix completion). Y =.

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Alignment of Noisy Unstructured Text Data

Alignment of Noisy Unstructured Text Data. Julien Bourdaillet, Jean-Gabriel Ganascia Pierre and Marie Curie University - Paris. Monolingual alignment Texts can be very different Textual genetic criticism Sequence processing algorithm: Suffix tree + A* Experiments: Very noisy text alignment

152 views • 1 slides