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A Lazy Introduction to AI for Infosec.
The simulation of human intelligence by computers is called AI. The simulation includes learning, understanding, logical reasoning, and improvisation.
Any AI that is created to perform only a specific set of tasks is called Artificial Narrow Intelligence. AI that is capable of self-correcting and making decisions exactly like a human is called Artificial General Intelligence. Real-time examples of artificial narrow intelligence include SIRI, Google Home, Alexa, IBM Watson, and Microsoft Cognitive Services.
Machine learning algorithms work by studying tons of data and updating their parameters to identify the patterns in that data. Ideally, we want the parameters of the machine learning models to encode general patterns (“patients who smoke are more likely to possess heart disease’’) instead of facts about specific training examples (“Alice Parker has heart disease”). Unfortunately, the algorithms don’t learn to ignore these specifics by default. If we would like to use machine learning to unravel such a crucial task, like making a cancer diagnosis model, then once we publish that machine learning model (for example, by making an open-source cancer diagnosis model for doctors around the globe to use) we may inadvertently reveal information about the training set. A malicious attacker could inspect the published model and learn private information about Alice Parker. This is where differential privacy comes in.
Differential privacy makes it possible for tech companies to collect and share aggregate information about user habits while maintaining the privacy of individual users. It is a framework for measuring the privacy guarantees provided by an algorithm. The key is a family of algorithms called Private Aggregation of Teacher Ensembles (PATE). OpenMined is an open-source community whose goal is to make the world more privacy-preserving by lowering the barrier-to-entry to private AI technologies. With OpenMined, an AI model can be governed by multiple owners and trained securely on an unseen, distributed dataset.
Now the BIG questions ahead are:
Harnessing the power of AI can open up endless possibilities in Cyber. But respecting the data privacy and setting standards for using the data should be given priority.
Although it’s a lazy intro but if it does make any sense, let’s start caring about our data. Stay tuned for The Rajappan Project.