Cover Image
QR Code

Data Science and Designing for Privacy.

Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing...

Full description

Published in:Techné: Research in Philosophy & Technology Vol. 20; no. 1; pp. 51 - 69
Main Author: Falgoust, Michael
Format: Article
Published: Philosophy Documentation Center 2016
Subjects:
Data science
Right of privacy
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=117764138&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 117764138
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        01617249
        2Y0N
      jtl: Techné: Research in Philosophy & Technology
      issn: 01617249
      maglogo: N
    pubinfo:
      dt: 2016
      vid: 20
      iid: 1
      pid: 2411
      pub: Philosophy Documentation Center
    artinfo:
      ui:
        117764138
        10.5840/techne201632446
      ppf: 51
      ppct: 18
      formats:
        fmt:
          @attributes:
            type: P
            size: 650KB
      tig:
        atl: Data Science and Designing for Privacy.
      aug:
        au: Falgoust, Michael
        affil: University of Twente, Drienerlolaan 5, 7522NB, Enschede, The Netherlands
      su:
        Data science
        Right of privacy
      sug:
        subj:
          Data science
          Right of privacy
      keyword:
        autonomy
        data science
        privacy
        value-sensitive design
      ab: Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacysensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts of informational privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      custom: Copyright of Techné: Research in Philosophy & Technology is the property of Philosophy Documentation Center and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use.
      item: Techné: Research in Philosophy & Technology
      holder: Philosophy Documentation Center
      dt:
        @attributes:
          year: 2016
    holdings:
      @attributes:
        islocal: N