He compares cybersecurity to the way in which U.S. states trying to catch bank robbers got a lot better at doing so once they all started sharing records of bank robberies with each other. That’s why, to explain why he believes Microsoft’s systems of protection and detection are superior, he goes back to a time before the internet. Once one state knew to be on the lookout for that bank robber, all the other states did, too.
In both security and privacy, Microsoft also has the luxury of thinking beyond responding to the short-term threats, and looking ahead to how the cloud can remain secure a decade or more in the future. That’s due in part to its worldwide network of research labs, which employs some of the world’s leading cryptographic and security researchers, Wing said.
All those foiled attacks, along with data about the hundreds of billions of emails and other pieces of information that flow to and from Microsoft’s cloud computing data centers, are constantly being fed into the company’s intelligent security graph.
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You want something consistent that can keep consensus across a variety of implementations, but also prevents cheap attacks, and finally is friendly to various virtual machine optimizations including pre-compilation and JIT execution. The problem with turing-completeness is that it is impossible to measure the cost of a smart contract until you actually execution it. This is definitely one of the reasons why it is still very much an open research problem. The best that can be done is to measure various pieces of code, and then you can at least know the gas price of a piece of code up until the next branch. At some point, by placing more and more code in the blockchain consensus system in order to properly measure the true resource cost of an execution, you actually spend more time measuring than you do executing the code. Code is data and can be mutated and overwritten. It may end up being better in the final implementation to have a mixed gas model, so that constant internal (ie, loaded like EVM code is now) bytecode has a more powerful model, but then code loaded and executed dynamically in read-write-execute memory uses a less computationally expensive (albeit less accurate/financially more expensive) gas model which can be used on-the-fly. This is actually one of the weaknesses of the x86 architecture for smart contracts. This is one of the hardest problems of gas models in general. These aspects come together to really form the most difficult aspect of this entire topic.
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