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Security and Legitimacy in a
Web Observatory
Kieron O’Hara, Alistair Sackley, Ian Brown,
Ramine Tinati, Thanassis Tiropanis & Xin Wang
presented at Workshop on Building Web Observatories,
WebSci14, Bloomington, Indiana, 23 June 2014
Complex England & Wales Policing
Context
• 43 regional police forces
• Several specialised national bodies
• E.g. Serious Fraud Office
• Many multi-agency partnerships
• E.g. Integrated Offender Management
• Data only loosely standardised
• Some brought together in national open data
site police.uk
• Police data v performance data
• Extremely sensitive personal data
Policing Requirements
• Highly data (intelligence) driven
• Need to retain legitimacy
• Policing by consent
• Greater scrutiny
• Cost constraints
• Control
• Data protection liability for personal data
• Big IT mindset
• Risk aversion
Data Management Requirements
• Effective
• The right people get the data at the right time
• Safe
• Data subjects (victims, witnesses) not exposed
• Secure
• Investigations not compromised
• Transparent
• Open to democratic scrutiny
Potential for Web Obs Contribution
to Data Management
• Control
• Security
• Privacy
• Discriminating
sharing
Potential for Contribution to
Engagement Strategy
Leigh Park Twitter mention network
(High IMD [index of multiple
deprivation])
Eastleigh Twitter mention network
(Diverse area with pockets of deprivation)
Potential for Contribution to Low-
Level Intelligence Gathering
• NOT surveillance, but understanding
• Only a small Twitter community in Leigh Park
• What are the appropriate sources of online data?
• Engagement means taking part (e.g.
retweeting)
• Placing police data in context of public and
open data
• Opening out some police data/analytics to
community groups, or even making it open
Need to Demonstrate
• Security
• Data protection
• Preservation of data control
• Potential of “safe haven”
• Utility
• Legitimacy
• Low/justified cost
• Compatibility with more traditional data
• Compatibility with workflow

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Security and Legitimacy in a Web Observatory: Requirements for Data Linkage, Sharing and Collection in Policing and Justice

  • 1. Security and Legitimacy in a Web Observatory Kieron O’Hara, Alistair Sackley, Ian Brown, Ramine Tinati, Thanassis Tiropanis & Xin Wang presented at Workshop on Building Web Observatories, WebSci14, Bloomington, Indiana, 23 June 2014
  • 2. Complex England & Wales Policing Context • 43 regional police forces • Several specialised national bodies • E.g. Serious Fraud Office • Many multi-agency partnerships • E.g. Integrated Offender Management • Data only loosely standardised • Some brought together in national open data site police.uk • Police data v performance data • Extremely sensitive personal data
  • 3. Policing Requirements • Highly data (intelligence) driven • Need to retain legitimacy • Policing by consent • Greater scrutiny • Cost constraints • Control • Data protection liability for personal data • Big IT mindset • Risk aversion
  • 4. Data Management Requirements • Effective • The right people get the data at the right time • Safe • Data subjects (victims, witnesses) not exposed • Secure • Investigations not compromised • Transparent • Open to democratic scrutiny
  • 5. Potential for Web Obs Contribution to Data Management • Control • Security • Privacy • Discriminating sharing
  • 6. Potential for Contribution to Engagement Strategy Leigh Park Twitter mention network (High IMD [index of multiple deprivation]) Eastleigh Twitter mention network (Diverse area with pockets of deprivation)
  • 7. Potential for Contribution to Low- Level Intelligence Gathering • NOT surveillance, but understanding • Only a small Twitter community in Leigh Park • What are the appropriate sources of online data? • Engagement means taking part (e.g. retweeting) • Placing police data in context of public and open data • Opening out some police data/analytics to community groups, or even making it open
  • 8. Need to Demonstrate • Security • Data protection • Preservation of data control • Potential of “safe haven” • Utility • Legitimacy • Low/justified cost • Compatibility with more traditional data • Compatibility with workflow