2017/05/21 by Mathias Lécuyer, Mathias Lecuyer, Lecuyer, Mathias +8
Computer Science · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #Data Stream Mining Techniques #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data #cs.CR
paper · pdf · doi:10.48550/arxiv.1705.07512
arxiv created 2017/05/21 · openalex publication_date 2017/05/21 · arxiv updated 2017/05/23 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed from data collected "just in case" would help these organizations to limit the latter's exposure to attack. A natural approach might be to monitor data use and retain only the working-set of in-use data in accessible storage; unused data can be evicted to a highly protected store. However, many of today's big data applications rely on machine learning (ML) workloads that are periodically retrained by accessing, and thus exposing to attack, the entire data store. Training set minimization methods, such as count featurization, are often used to limit the data needed to train ML workloads to improve performance or scalability. We present Pyramid, a limited-exposure data management system that builds upon count featurization to enhance data protection. As such, Pyramid uniquely introduces both the idea and proof-of-concept for leveraging training set minimization methods to instill rigor and selectivity into big data management. We integrated Pyramid into Spark Velox, a framework for ML-based targeting and personalization. We evaluate it on three applications and show that Pyramid approaches state-of-the-art models while training on less than 1% of the raw data.