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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:IC Colloquium : Query-Based Data Pricing
DTSTART:20130121T161500
DTEND:20130121T173000
DTSTAMP:20260408T023510Z
UID:90d57d4dc72433a542f21c1a9c416c72b92380bb07742f26eec66ff4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dan Suciu\, University of Washington\nAbstract\nData has value
 \, and is increasingly being bought and sold on the Web. Some large data v
 endors are producing highly valuable data in-house and selling it directly
  to customers\, e.g.  Gartner reports or Navteq maps. Smaller vendors are
  producing data often by aggregating public sources and sell it on Data Ma
 rkets\, such as Azure DataMarkets or Aggdata. And personal data lockers ce
 ntralize private data with the goal of allow end-users to profit from thei
 r use\, e.g. personal.com or lockerproject.org.  Current pricing mechanis
 ms\, however\, are very naive. By far the most common case is that of a fi
 xed price for the entire data set. The big stomers can typically afford to
  purchase the data they need (e.g. the price of one Gartner Report is in t
 he range of thousand of dollars)\, but small customers often need only a f
 ew data items from the entire data set and cannot afford to pay the full p
 rice.\nIn this talk I will discuss a framework for pricing data that allow
 s the seller to set explicit prices for a set of views of her choice\, and
  allows the buyer to buy ANY query\; the price of the query is derived aut
 omatically from the explicit prices set by the seller.  We call this fram
 ework ``query-based pricing''. A pricing function must satisfy an importan
 t property: it must be "arbitrage-free"\, in the sense that it must preven
 t the buyer from obtaining the answer to some query by purchasing and comb
 ining cheaper queries.  In the case of traditional\, conjunctive queries 
 on a relational database\, arbitrage-freeness is related to "query-view de
 terminacy"\, a concept that has been well studied in database theory. In 
 the case when the queries are perturbed answers to linear queries over pri
 vate data\, arbitrage-freeness is related to the "privacy budget" that has
  been studied in the context of differential privacy.  I will show the th
 eoretical complexity of computing an arbitrage-free price (which is high)\
 , as well as a practical way to circumvent the high complexity. Joint work
  with M. Balazinska\, B. Howe\, P. Koutris\, Daniel Li\, Chao Li\, G. Mikl
 au\, P. Upadhyaya.Biography\nDan Suciu is a Professor in Computer Science 
 at the University of Washington. He received his Ph.D. from the University
  of Pennsylvania in 1995\, was a principal member of the technical staff a
 t AT&T Labs and joined the University of Washington in 2000. Suciu is cond
 ucting research in data management\, with an emphasis on topics related to
  Big Data and data sharing\, such as probabilistic data\, data pricing\, p
 arallel data processing\, data security. He is a co-author of two books Da
 ta on the Web: from Relations to Semistructured Data and XML\, 1999\, and 
 Probabilistic Databases\, 2011. He is a Fellow of the ACM\, holds twelve U
 S patents\, received the ACM SIGMOD Best Paper Award in 2000\, the ACM POD
 S Alberto Mendelzon Test of Time Award in 2010 and in 2012\, and is a reci
 pient of the NSF Career Award and of an Alfred P. Sloan Fellowship. Suciu 
 serves on the VLDB Board of Trustees\, and is an associate editor for the 
 VLDB Journal\, ACM TOIS\, ACM TWEB\, and Information Systems and is a past
  associate editor for ACM TODS. Suciu's PhD students Gerome Miklau and Chr
 istopher Re received the ACM SIGMOD Best Dissertation Award in 2006 and 20
 10 respectively\, and Nilesh Dalvi was a runner up in 2008.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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