'Random permutation' diaporamas de présentation

Cryptography

Cryptography. Lecture 5 Stefan Dziembowski www.dziembowski.net stefan@dziembowski.net. Plan. Introduciton to authentica tion CBC-MAC Introduction to the collision-resistant hash functions NMAC and HMAC Authentication + Encryption. Message Authentication. Integrity:. M. Alice. Bob.

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ET4085/ET5085 Keamanan Jaringan Telekomunikasi ET4085/ET5085 Telecommunication Network Security

ET4085/ET5085 Keamanan Jaringan Telekomunikasi ET4085/ET5085 Telecommunication Network Security . Tutun Juhana Telecommunication engineering School of electrical engineering & informatics Institut teknologi bandung http://telecommunication.itb.ac.id/~tutun/ET4085. Cryptography.

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Network Security Essentials Chapter 2: Symmetric Encryption

Network Security Essentials Chapter 2: Symmetric Encryption. Fourth Edition by William Stallings (Based on Lecture slides by Lawrie Brown ). Outline. Symmetric encryption Block encryption algorithms Stream ciphers Block cipher modes of operations. Symmetric Encryption.

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Linear Programming in Low Dimensions

Linear Programming in Low Dimensions. Presented by Nati Srebro. Linear Programming. Linear Programming in 2D. Linear Programming in 2D. An Infeasible Linear Program. An Unbounded LP. An Unbounded LP. Unique optimum Optimal edge Unbounded Infeasible. Types of LPs. Find the optimum

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Molecular Classification of Cancer

Molecular Classification of Cancer. Class Discovery and Class Prediction by Gene Expression Monitoring. Overview. Motivation Microarray Background Our Test Case Class Prediction Class Discovery. Motivation. Importance of cancer classification

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The Cover Time of Random Walks

The Cover Time of Random Walks. Uriel Feige Weizmann Institute. Random Walks. Simple graph. Move to a neighbor chosen uniformly at random. Random Walks. Random Walks. Random Walks. Random Walks. Random Walks. Random Walks. Random Walks. Hitting time and its variants.

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Packing bag-of-features

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COSC 6114

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Low-Support, High-Correlation

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Quantum cryptography

Quantum cryptography. CS4600/5600 Biometrics and Cryptography UTC/CSE. Introduction. Light waves are propagated as discrete particles known as photons . Polarization of the light is carried by the direction of the angular momentum, or spin of the photons. .

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Provable Security

Provable Security. Examples: one- wayness. Given a ciphertext , find the corresponding plaintext. Example : Indistinguishability. Distinguish between encryptions of 2 chosen plaintexts. Example : Non-malleability. Given a ciphertext , find another ciphertext s.t.2 ciphertexts related.

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Competitive Viral Marketing

Competitive Viral Marketing . Based on: S. Bharathi et al. Competitive Influence Maximization in Social Networks , WINE 2007. C. Budak et al. Limiting the spread of misinformation in social networks. In WWW'11.

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Near Duplicate Detection

Near Duplicate Detection. Slides adapted from Information Retrieval and Web Search, Stanford University, Christopher Manning and Prabhakar Raghavan CS345A, Winter 2009: Data Mining. Stanford University, Anand Rajaraman , Jeffrey D. Ullman. Sec. 19.4.1. User. Web spider. Search. Indexer.

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Explicit Feature Methods for Accelerated Kernel Learning

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More Stream-Mining

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A lightweight secure protocol for wireless sensor networks

A lightweight secure protocol for wireless sensor networks. ??? 2007.12. 4 ELSEVIER Mar. 2006. Contents. Introduction Security goals Assumption LCG-based security protocols Performance analysis Conclusions and future work. Introduction (1/2). Linear Congruential Generator (LCG)

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Probabilistic Analysis and Randomized Algorithm

Probabilistic Analysis and Randomized Algorithm. Average-Case Analysis. In practice, many algorithms perform better than their worse case The average case is analyzed by Construct a probabilistic model of their input

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Computational Statistics with Application to Bioinformatics

Computational Statistics with Application to Bioinformatics. Prof. William H. Press Spring Term, 2008 The University of Texas at Austin Unit 9: Working with Multivariate Normal Distributions. The multivariate normal distribution completely defined by its mean (vector) and covariance (matrix)

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How should a computer shuffle?

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Binary Planar Partition

Binary Planar Partition. Lecture 2 Advanced Algorithms II. Slides by: Arjun Dasgupta. Slides by: Arjun Dasgupta. Example 1. l1. 1. 3. l2. l3. 2. 4. Each oval node stores information about the infinite line l i The leaves denote the line segments being partitioned. Example 2. l1.

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