From
the abstract: "Recent computer science research on the reidentification of
individuals from anonymized data has given some observers in the legal
community the impression that the utilization of data is incompatible with
strong privacy
guarantees, leaving few options for balancing privacy
and utility in various data-intensive settings. This bleak assessment is
incomplete and somewhat misleading, however, because it fails to recognize the
promise of technologies that support anonymity under a standard that computer
scientists call differential privacy. This standard
is met by a database system that behaves similarly whether or not any
particular individual is represented in the database, effectively producing
anonymity. Although a number of computer scientists agree that these
technologies can offer privacy-protecting
advantages over traditional approaches such as redaction of personally
identifiable information from shared data, the legal community’s critique has
focused on the burden that these technologies place on the utility of the data.
Empirical evidence, however, suggests that at least one highly successful
business, Facebook, has implemented such privacy-preserving
technologies in support of anonymity promises while also meeting commercial
demands for utility of certain shared data.
This Article uses a reverse-engineering approach to infer that Facebook appears to be using differential privacy-supporting technologies in its interactive query system to report audience reach data to prospective users of its targeted advertising system, without apparent loss of utility." Read more
This Article uses a reverse-engineering approach to infer that Facebook appears to be using differential privacy-supporting technologies in its interactive query system to report audience reach data to prospective users of its targeted advertising system, without apparent loss of utility." Read more