Privacy in Electronic Society

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Privacy in Electronic Society
Lingyu Wang
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Privacy Concerns Press, Government and Academy

• Economist (1999) The End of Privacy
• Federal Trade Commission Privacy Initiatives
  (http//www.ftc.gov/privacy/)
• Platform for Privacy Preferences (P3P) Project
  (http//www.w3.org/P3P/)
• Internet Privacy Communications of the ACM
  special issue (1999)
• Workshop on Privacy in the Electronic Society
  (D.C. 2002)

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Privacy Concerns Public

• 33.5 of users provided falsified personal
  information due to the fear of privacy breach
• 70.15 of users that do not register are because
  of unclear privacy policies
• Most users prefer anonymous payment system (rated
  3.93 out of 5 )
• Data source 6th WWW Survey by GVU (Graphic,
  Visualization, Usability Center) at Galtech

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Privacy Concern - Businesses

• Only public and government want privacy? No
• The absence of privacy rules imposes expenses on
  businesses
• For example, consumers routinely abandon shopping
  carts on websites because of demands for too much
  personal information
• Analysts estimate that Internet retail sales lost
  due to privacy concerns may be as much as 18
  billion (report by Robert Gellman)

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Two Aspects of Privacy in Electronic Society

• Collecting users private information is
  collected during an electronic transaction
• For example, registering at an e-shop needs name,
  mailing addr., telephone, DOB, mothers maiden
  name, etc.
• Disclosing collected information is disclosed to
  third party
• For example, sales data are provided to another
  company for data analysis and data mining purpose

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Open Issues - Collecting Stage

• Current information collecting policies of
  merchants are not flexible
• No mechanism for users to bargain with merchants
  about providing personal data
• Either provide everything asked, or leave the
  site
• Many collected information are not necessary for
  the transaction

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Open Issues Disclosing Stage

• Currently data are disclosed with little
  protection
• Data sanitization is not enough (e.g., name is
  not the only identifier)
• Summarized statistical data could be sensitive
  (e.g., average salary by departments and gender
  consider a department with only one female
  employee)