Data Monitoring Confidentialityand the Grid

Mark ElliotConfidentiality 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.

PRE-ACCESS DQI Monitor

Raw Data

Treated Data

Data Intrusion

sentry

Analytical Requests

PRE-OUTPUT SDRA/SDC

PRE-ACCESS SDRA/SDC

PRE-Output DQI Monitor

AnalyticalOutput

Firewall

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

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