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The Algorithmic Foundations of Differential Privacy

The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.

Barely Missing Everything

“There are moments when a story shakes you...Barely Missing Everything is one of those stories, and Mendez, a gifted storyteller with a distinct voice, is sure to bring a quake to the literary landscape.” —Jason Reynolds, New York Times bestselling author of Long Way Down In the tradition of Jason Reynolds and Matt de la Peña, this heartbreaking, no-holds-barred debut novel told from three points of view explores how difficult it is to make it in life when you—your life, brown lives—don’t matter. Juan has plans. He’s going to get out of El Paso, Texas, on a basketball scholarship and make something of himself—or at least find something better than his mom Fabi’s cruddy apartment, her string of loser boyfriends, and a dead dad. Basketball is going to be his ticket out, his ticket up. He just needs to make it happen. His best friend JD has plans, too. He’s going to be a filmmaker one day, like Quentin Tarantino or Guillermo del Toro (NOT Steven Spielberg). He’s got a camera and he’s got passion—what else could he need? Fabi doesn’t have a plan anymore. When you get pregnant at sixteen and have been stuck bartending to make ends meet for the past seventeen years, you realize plans don’t always pan out, and that there are some things you just can’t plan for… Like Juan’s run-in with the police, like a sprained ankle, and a tanking math grade that will likely ruin his chance at a scholarship. Like JD causing the implosion of his family. Like letters from a man named Mando on death row. Like finding out this man could be the father your mother said was dead. Soon Juan and JD are embarking on a Thelma and Louise­—like road trip to visit Mando. Juan will finally meet his dad, JD has a perfect subject for his documentary, and Fabi is desperate to stop them. But, as we already know, there are some things you just can’t plan for…

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