Data monitoring confidentiality and the grid
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Data Monitoring Confidentiality and the Grid. Mark Elliot Confidentiality And Privacy Group (www.ccsr.ac.uk/capri) University of Manchester. Overview. Data Data Everywhere…. The Grid and its potential New confidentiality problems and opportunities Data Environment Analysis.

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Data Monitoring Confidentialityand the Grid

Mark Elliot

Confidentiality And Privacy Group

(www.ccsr.ac.uk/capri)

University of Manchester


Overview

  • Data Data Everywhere….

  • The Grid and its potential

  • New confidentiality problems and opportunities

  • Data Environment Analysis


Data Data Everywhere…

  • Massive and exponential increase in data; Mackey and Purdam(2002); Purdam and Elliot(2002).

    • These studies have led to the setting up of the data monitoring service.

  • Singer(1999) noted three behavioural tendencies:

    • Collect more information on each population unit

    • Replace aggregate data with person specific databases

    • Given the opportunity collect personal information

  • Purdam and Elliot add:

    • Link data whenever you can


The Grid

  • “Integrated infrastructure for high-performance distributed computation” Cannataro and Talia (2002)

    • Grid middleware handles the technical issues communication, security, access/authentication etc… Cole et al (2002)

  • Data grid

  • Knowledge grid


A Blurring of Concepts

  • The boundaries between data and processes become less distinct

    • Non-static datasets

    • One persons output is another person’s data


Combining and Enhancing Data

  • Record linkage

  • Data fusion

  • Simulation

  • Verification

    • Of data

    • Of output


Data Mining and the Grid

  • Traditional Data Mining examines and identifies patterns on single (if massive) datasets.

  • But Data Mining is really a method/ approach/ technology that has been waiting for the grid to happen. Multi dataset mining is now becoming a reality.


Agents

  • AI concept

  • Active programs capable of directed ‘intelligent’ search and manipulation. Web crawlers

  • Building blocks of dynamic grid?


A Look Over the Horizon

  • Absolute Seamlesness.

    • The ability to sit at a computer/terminal and request the information one requires.

      • In natural language.

  • Real-time dynamic modelling and simulation.


But…………

  • Human issues

    • Closer to artificial consciousness

      • Admit machines into our moral universe

    • Technological Interdependence

    • Confidentiality and privacy


Confidentiality issues and opportunities

  • Data Linkage increases disclosure risk

    BUT

  • Indirect Data Access allows a new method of controlling disclosure and increase analytical power.


Tentative Architecture for complete system for disclosure control in remote access systems.

Firewall

Raw Data

PRE-ACCESS DQI Monitor

PRE-ACCESS SDRA/SDC

Treated Data

PRE-Output DQI Monitor

PRE-OUTPUT SDRA/SDC

Data Intrusionsentry

Analytical

Requests

Analytical

Output


Data Environment Analysis

  • Need to move with the technology from:

    • One shot analyses of individual datasets

    • Ongoing analyses of the data environment

  • The question is Not how safe is my data but how disclosive is the data environment.

  • A process of data monitoring is one aspect of this.


What sort of society?

  • Informational Transparency?

  • Human- Computer Interdependence?

  • Individualism vs Collectivism

  • A choice:

  • More legislation or less?

  • Personal information a commodity or public good


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