Privacy and the price of data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Several important application areas in data science involve assigning numbers to (possibly randomized) algorithms. In the case of statistical privacy, it is important to quantify the amount of information leaked by a data processing algorithm. In the case of data marketplaces, it is important to properly set the prices for data queries (which, in general, can be specified by arbitrary algorithms). In both cases, it is also important to quantify application-specific utility of the outputs of an algorithm or query. For example, if a user has a choice of purchasing some combination of query answers, the user's decision should be guided by the consideration of a utility/price trade-off. These numbers cannot be assigned to algorithms in an arbitrary manner - there are common-sense restrictions that must be enforced. For example, the answer to an expensive query should not be derivable from a much cheaper query. Each application has its own set of restrictions but when they are formulated mathematically, common patterns emerge. Thus technical results from one area can often be ported to the others.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781479988754
StatePublished - Jul 31 2015
Event30th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2015 - Kyoto, Japan
Duration: Jul 6 2015Jul 10 2015

Publication series

NameProceedings - Symposium on Logic in Computer Science
ISSN (Print)1043-6871


Other30th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2015

All Science Journal Classification (ASJC) codes

  • Software
  • General Mathematics


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