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Introduction to support vectors In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. What are support vectors Support vectors are the data points that lie closest to the decision surface (or hyperplane) • They are the data points most difficult to classify • They have direct bearing on the optimum location of the decision surface • We can show that the optimal hyperplane stems from the function class with the lowest “capacity”= # of independent features/parameters Theoretical concept SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane.  • The decision function is fully specified by a (usually very small) subset of training samples, the support vectors.  • This becomes a Quadratic programming problem that is easy to solve by standard methods Separation by Hyperplanes • Assume linear

A Case Study On End-to-End Encryption Used In Whatsapp 1.Introduction to End-to-End Encryption WhatsApp's end-to-end encryption is available when you and the people you message use the latest versions of the app. WhatsApp's end-to-end encryption ensures only you and the person you're communicating with can read what is sent, and nobody in between, not even WhatsApp. This is because your messages are secured with a lock, and only the recipient and you have the special key needed to unlock and read them. For added protection, every message you send has its own unique lock and key. WhatsApp, since its inception six years ago, has quickly grown into a global phenomenon, becoming one of the most popular mobile based communications applications in the world today. With a user base that eclipsed one billion in February, WhatsApp provides a service that potentially endangers the privacy of over 10% of the entire human population. In order to address these security concern