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zkML IRL: Practical use cases for more secure models

aleo.org · 85 words · saved by 1 readers

Machine learning models rely on large amounts of data to churn out accurate outputs that appear almost magical in their ability to understand us. But, as the amount of data ingested into machine learning projects grows, privacy flaws will become more and more apparent — and there may be a tipping point where users are no longer willing to trade their privacy for the output. Before the industry develops further, models need to have a layer of accountability built in. Users need to trust that their data won’t be abused, that the model hasn’t been hacked or altered, and that models meet industry data privacy standards. Right now, the standard isn’t being met — but we have the opportunity to change that. Zero-knowledge proofs are one way for developers to create and run machine learning models that prove a computation was done correctly while being free to choose which properties to make public. The result is the best of both worlds: interesting, personalized outputs based on secure, trust

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