'Privacy preserving' diaporamas de présentation

Evaluating LBS Privacy In DYNAMIC CONTEXT

Evaluating LBS Privacy In DYNAMIC CONTEXT. Outline. Introduction Overview Attack Model Classification Defend Model Evaluation Module Conclusion. Outline. Introduction Overview Attack Model Classification Defend Model Evaluation Module Conclusion. What we need now?.

935 views • 84 slides

PRIN 2004 Project GeoPKDD

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Privacy Preserving Data Mining Lecture 1 Motivating privacy research, Introducing Crypto

Privacy Preserving Data Mining Lecture 1 Motivating privacy research, Introducing Crypto. Benny Pinkas HP Labs, Israel. Course structure. Lecture 1: Introduction to privacy Introduction to cryptography , in particular, to rigorous cryptographic analysis. Definitions Proofs of security

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Privacy Preserving Data Mining Lecture 1 Motivating privacy research, Introducing Crypto

Privacy Preserving Data Mining Lecture 1 Motivating privacy research, Introducing Crypto. Benny Pinkas HP Labs, Israel. Course structure. Lecture 1: Introduction to privacy Introduction to cryptography , in particular, to rigorous cryptographic analysis. Definitions Proofs of security

504 views • 37 slides

Managing the Assured Information Sharing Lifecycle

Managing the Assured Information Sharing Lifecycle. Tim Finin, UMBC 08 June 2009. http://aisl.umbc.edu/show/resource/id/498/. 2008 MURI project. University of Maryland, Baltimore County (Lead Inst.) T. Finin (Lead), A. Joshi, H. Kargupta, A. Sherman, Y. Yesha Purdue University

440 views • 34 slides

A N ovel Framework for LBS Privacy Preserving in Dynamic Context Environment

A N ovel Framework for LBS Privacy Preserving in Dynamic Context Environment. ACOMP 2011. Le Nguyen Duy Vu Nguyen Le Vinh Nguyen Ngoc Tuan Do Son Thanh Tran Trung Hien Dang Tran Khanh. Outline. Location-based services: privacy concerns in dynamic-context environment

473 views • 33 slides

M-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets

M-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets. by Tyrone Cadenhead. Main Contribution. Paper presents study on privacy preserving publication of fully-dynamic datasets. These datasets can be modified by any sequence of insertions and deletions.

470 views • 31 slides

FAIR, PRIVACY- PRESERVING AND SECURE AI

Please write title, subtitle and speaker name in all capital letters. FAIR, PRIVACY- PRESERVING AND SECURE AI. STRATA 2019, LONDON. MIKIO L. BRAUN. 01-05-2019. AI IS STARTING TO IMPACT OUR LIVES. M. BRAUN, FAIR, PRIVACY PRESERVING AND SECURE AI, STRATA LONDON 2019. BIAS AND FAIRNESS.

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Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data Presenter ? ? ?

Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data Presenter ? ? ?. INTRODUCTION PROBLEM FORMULATION FRAMEWORK AND PRIVACY REQUIREMENTS FOR MRSE PRIVACY-PRESERVING AND EFFICIENT MRSE PERFORMANCE ANALYSIS RELATED WORK CONCLUSION. INTRODUCTION. coordinate matching

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Big Data, Big Commerce, Big Challenge

Big Data, Big Commerce, Big Challenge. Reporter ? Ximeng Liu. Supervisor: Rongxing Lu. School of EEE, NTU. http://www.ntu.edu.sg/home/rxlu/seminars.htm. Outline. GOOD: Challenge:. BIG DATA ? COMMERCE IN DATA ? BIG MONEY. BIG DATA ? BIG PROBLEM ? BIG SECURITY ISSUE. Big Data.

467 views • 21 slides

Privacy Preserving In LBS

Privacy Preserving In LBS. Evaluating Privacy of LBS Algorithms In Dynamic Context. Outline. Introduction Design Model & Workflow System Design Specification : General Approach Build Privacy Case Based Database Conclusion & Future Work. Design Specification : General Approach.

374 views • 17 slides

International Workshop ISDSI 09 June 25th, 2009, Camogli

International Workshop ISDSI 09 June 25th, 2009, Camogli. A Framework for Privacy-Preserving Face Matching. Authors: Filippo Volpi, Monica Scannapieco, Tiziana Catarci. filvolpi@gmail.com scannapi@istat.it catarci@dis.uniroma1.it. Scenario.

292 views • 17 slides

ebiquity.umbc/

UMBC and CBMANET. http://ebiquity.umbc.edu/. Anupam Joshi and Tim Finin Ebiquity Research Group University of Maryland, Baltimore County Baltimore MD 21250. Overview. Who we are We briefly introduce UMBC and our research groups: ebiquity, dawn, diadic What motivates our research agenda

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239 views • 15 slides

Study on Health Care IT infrastructure

Study on Health Care IT infrastructure. Presented by Samuel Kasimalla Under Supervision of Dr Box. Topics covered. Planning and Implementation a project Phases of implementing a health care based large project. Various published frameworks Cloud based approach

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Introduction of Sakurai Lab.

Introduction of Sakurai Lab. Kyushu Univ. JAPA N. ITSLAB ( I nformation T echnology & S ecurity Lab oratory). Address Department of Computer Science and Communication Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka 812-8581, Japan. This building.

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Cost Effective Privacy Preserving Of Intermediate Data Set In Cloud Storage

Data intensive applications store their valuable intermediate datasets in cloud in order to save the cost of re-computing. This poses a risk on data privacy protection because malicious parties may deduce the private information of the original datasets by analyzing multiple intermediate datasets. This system is implemented based on the least frequent pattern mining algorithm to identify the least frequent table and thereby encrypting it.

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Introduction of Sakurai Lab.

Introduction of Sakurai Lab. Kyushu Univ. JAPA N. ITSLAB ( I nformation T echnology & S ecurity Lab oratory). Address Department of Computer Science and Communication Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, Fukuoka 812-8581, Japan. This building.

103 views • 10 slides

Privacy Preserving Approaches for High Dimensional Data

This paper proposes a model for hiding sensitive association rules for Privacy preserving in high dimensional data. Privacy preservation is a big challenge in data mining. The protection of sensitive information becomes a critical issue when releasing data to outside parties. Association rule mining could be very useful in such situations. It could be used to identify all the possible ways by which 'non confidential' data can reveal 'confidential' data, which is commonly known as 'inference problem'. This issue is solved using Association Rule Hiding ARH techniques in Privacy Preserving Data Mining PPDM . Association rule hiding aims to conceal these association rules so that no sensitive information can be mined from the database. Tata Gayathri | N Durga "Privacy Preserving Approaches for High Dimensional Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: https://www.ijtsrd.com/papers/ijtsrd2430.pdf Paper URL: http://www.ijtsrd.com/engineering/computer-engineering/2430/privacy-preserving-approaches-for-high-dimensional-data/tata-gayathri

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Max Welling

224 views • 4 slides