2020/08/31 by Raul Castro Fernandez, Fernandez, Raul Castro, Kyle Chard +19 · 2 citations
Computer Science · Decision Sciences · Engineering · #Architecture #Blockchain Technology Applications and Security #Business #Computer science #Computer security #Data Quality and Management #Data access #Data collection #Data governance #Data mining #Data quality #Data science #Data security #Data sharing #Data virtualization #Data warehouse #Database #Databases (cs.DB) #Encryption #Engineering #FOS: Computer and information sciences #Finance #Incentive #Key (lock) #Market data #Privacy-Preserving Technologies in Data #cs.DB
paper · pdf · doi:10.48550/arxiv.2009.00035
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/08/31 · openalex publication_date 2020/08/31 · arxiv updated 2020/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper introduces Data Stations, a new data architecture that we are designing to tackle some of the most challenging data problems that we face today: access to sensitive data; data discovery and integration; and governance and compliance. Data Stations depart from modern data lakes in that both data and derived data products, such as machine learning models, are sealed and cannot be directly seen, accessed, or downloaded by anyone. Data Stations do not deliver data to users; instead, users bring questions to data. This inversion of the usual relationship between data and compute mitigates many of the security risks that are otherwise associated with sharing and working with sensitive data. Data Stations are designed following the principle that many data problems require human involvement, and that incentives are the key to obtaining such involvement. To that end, Data Stations implement market designs to create, manage, and coordinate the use of incentives. We explain the motivation for this new kind of platform and its design.