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Protecting vast quantities of data poses a daunting challenge for the growingnumber of organizations that collect, stockpile, and monetize it. The abilityto distinguish data that is actually needed from data collected "just in case"would help these organizations to limit the latter's exposure to attack. Anatural approach might be to monitor data use and retain only the working-setof in-use data in accessible storage; unused data can be evicted to a highlyprotected store. However, many of today's big data applications rely on machinelearning (ML) workloads that are periodically retrained by accessing, and thusexposing to attack, the entire data store. Training set minimization methods,such as count featurization, are often used to limit the data needed to trainML workloads to improve performance or scalability. We present Pyramid, alimited-exposure data management system that builds upon count featurization toenhance data protection. As such, Pyramid uniquely introduces both the idea andproof-of-concept for leveraging training set minimization methods to instillrigor and selectivity into big data management. We integrated Pyramid intoSpark Velox, a framework for ML-based targeting and personalization. Weevaluate it on three applications and show that Pyramid approachesstate-of-the-art models while training on less than 1% of the raw data.

Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization