Privacy-by-Design: Understanding Data Access Models for Secondary Data.

摘要: Today there is a constant flow of data into, out of, and between ever-larger ever-more complex databases about people. Together, these digital traces collectively capture our social genome , the footprints society. The burgeoning field population informatics systematic study populations via secondary analysis such massive collections (termed “big data”) In particular, health analyzes electronic records to improve outcomes for population. Privacy protection in research requires holistic approach which combines technology, statistics, policy shift culture information accountability through transparency rather than secrecy. We review state art privacy technology frameworks from widely different fields, synthesize findings present comprehensive system using privacy-by-design approach. Based on common activities workflow, we describe pros cons four access models – restricted access, controlled monitored open that minimize risk maximize usability data. then evaluate by analyzing realistic example. conclude deployed together can provide protection, balancing research.