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Data Warehousing
Data Mining
Privacy
Reading
Farkas
CSCE 824 - Spring 2011
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Data Warehousing
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Repository of data providing
organized and cleaned enterprisewide data (obtained form a
variety of sources) in a
standardized format
– Data mart (single subject area)
– Enterprise data warehouse (integrated
data marts)
– Metadata
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OLAP Analysis
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Aggregation functions
Factual data access
Complex criteria
Visualization
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Warehouse Evaluation
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Enterprise-wide support
Consistency and integration
across diverse domain
Security support
Support for operational users
Flexible access for decision
makers
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Data Integration
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Data access
Data federation
Change capture
Need ETL (extraction,
transformation, load)
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Data Warehouse Users
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Internal users
– Employees
– Managerial
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External users
– Reporting and auditing
– Research
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Data Mining
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Databases to be mined
Knowledge to be mined
Techniques Used
Applications supported
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Data Mining Task
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Prediction Tasks
– Use some variables to predict
unknown or future values of other
variables
Description Tasks
– Find human-interpretable patterns
that describe the data
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Common Tasks
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Classification [Predictive]
Clustering [Descriptive]
Association Rule Mining [Descriptive]
Sequential Pattern Mining [Descriptive]
Regression [Predictive]
Deviation Detection [Predictive]
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Security for Data
Warehousing
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Establish organizations security
policies and procedures
Implement logical access control
Restrict physical access
Establish internal control and
auditing
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Security for Data
Warehousing (cont.)
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Security Issues in Data Warehousing and
Data Mining: Panel Discussion
Panel discussion of Bhavani Thuraisingham,
The MITRE Corporation, Linda Schlipper,
The MITRE Corporation, Pierangela
Samarati, SRI International, T. Y. Lin, San
Jose State University, Sushil Jajodia, George
Mason University, Chris Clifton, The MITRE
Corporation,
xanadu.cs.sjsu.edu/~tylin/publications/pape
rList/109_security.ps
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Integrity
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Poor quality data: inaccurate,
incomplete, missing meta-data
Source data quality vs. derived
data quality
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Access Control
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Layered defense:
– Access to processes that extract
operational data
– Access to data and process that
transforms operational data
– Access to data and meta-data in the
warehouse
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Access Control Issues
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Mapping from local to warehouse
policies
How to handle “new” data
Scalability
Identity Management
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Inference Problem
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Data Mining: discover “new knowledge”  how to
evaluate security risks?
Example security risks:
– Prediction of sensitive information
– Misuse of information
Assurance of “discovery”
Interesting Read: C. C. Aggarwal and P.S. Yu,
PRIVACY-PRESERVING DATA MINING: MODELS
AND ALGORITHMS,
http://charuaggarwal.net/toc.pdf
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Privacy
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Large volume of private (personal) data
Need:
– Proper acquisition, maintenance,
usage, and retention policy
– Integrity verification
– Control of analysis methods
(aggregation may reveal sensitive
data)
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Privacy
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What is the difference between
confidentiality and privacy?
Identity, location, activity, etc.
Anonymity vs. accountability
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Legislations
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Privacy Act of 1974, U.S. Department of Justice
(http://www.usdoj.gov/oip/04_7_1.html )
Family Educational Rights and Privacy Act (FERPA),
U.S. Department of Education,
(http://www.ed.gov/policy/gen/guid/fpco/ferpa/in
dex.html )
Health Insurance Portability and Accountability Act
of 1996 (HIPAA),
(http://en.wikipedia.org/wiki/Health_Insurance_Por
tability_and_Accountability_Act )
Telecommunications Consumer Privacy Act
(http://www.answers.com/topic/electroniccommunications-privacy-act )
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Online Social Network
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Social Relationship
 Communication context changes
social relationships
 Social relationships maintained
through different media grow at
different rates and to different
depths
 No clear consensus which media is
the best
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Internet and Social
Relationships
Internet
 Bridges distance at a low cost
 New participants tend to “like” each
other more
 Less stressful than face-to-face
meeting
 People focus on communicating
their “selves” (except a few
malicious users)
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Social Network
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Description of the social structure
between actors
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Connections: various levels of social
familiarities, e.g., from casual
acquaintance to close familiar bonds
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Support online interaction and
content sharing
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Social Network Analysis
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The mapping and measuring of
relationships and flows between
people, groups, organizations,
computers or other information
processing entities
Behavioral Profiling
Note: Social Network Signatures
– User names may change, family and
friends are more difficult to change
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Interesting Read:
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Farkas
M. Chew, D. Balfanz, B. Laurie,
(Under)mining Privacy in Social
Networks,
http://citeseer.ist.psu.edu/viewd
oc/summary?doi=10.1.1.149.446
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Next Hippocratic
Databases
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Next Class
Stream Data
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