Data and Applications Security Developments and Directions

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Data and Applications Security Developments and
Directions

• Dr. Bhavani Thuraisingham
• The University of Texas at Dallas
• Lecture 9
• Data Mining, Security and Privacy
• March 21, 2007

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Objective of the Unit

• This unit provides an overview of data mining for
  security (national security) and then discusses
  privacy

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Data Mining for Counter-terrorism
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Data Mining Needs for Counterterrorism
Non-real-time Data Mining

• Gather data from multiple sources
• Information on terrorist attacks who, what,
  where, when, how
• Personal and business data place of birth,
  ethnic origin, religion, education, work history,
  finances, criminal record, relatives, friends and
  associates, travel history, . . .
• Unstructured data newspaper articles, video
  clips, speeches, emails, phone records, . . .
• Integrate the data, build warehouses and
  federations
• Develop profiles of terrorists,
  activities/threats
• Mine the data to extract patterns of potential
  terrorists and predict future activities and
  targets
• Find the needle in the haystack - suspicious
  needles?
• Data integrity is important
• Techniques have to SCALE

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Data Mining for Non Real-time Threats
Clean/
Integrate
Build
modify
data
Profiles
data
of Terrorists
sources
and Activities
sources
Mine
Data sources
the
with information
about terrorists
data
and terrorist activities
Report
Examine
final
results/
results
Prune
results
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Data Mining Needs for Counterterrorism
Real-time Data Mining

• Nature of data
• Data arriving from sensors and other devices
• Continuous data streams
• Breaking news, video releases, satellite images
• Some critical data may also reside in caches
• Rapidly sift through the data and discard
  unwanted data for later use and analysis
  (non-real-time data mining)
• Data mining techniques need to meet timing
  constraints
• Quality of service (QoS) tradeoffs among
  timeliness, precision and accuracy
• Presentation of results, visualization, real-time
  alerts and triggers

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Data Mining for Real-time Threats
Rapidly
Integrate
Build
sift through
data and
data
real
-
time
discard
models
sources in
irrelevant
real
-
time
data
Mine
Data sources
the
with information
about terrorists
data
and terrorist activities
Report
Examine
final
Results in
results
Real
-
time
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Data Mining Outcomes and Techniques for
Counter-terrorism
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Example Success Story - COPLINK

• COPLINK developed at University of Arizona
• Research transferred to an operational system
  currently in use by Law Enforcement Agencies
• What does COPLINK do?
• Provides integrated system for law enforcement
  integrating law enforcement databases
• If a crime occurs in one state, this information
  is linked to similar cases in other states
• It has been stated that the sniper shooting case
  may have been solved earlier if COPLINK had been
  operational at that time

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Where are we now?

• We have some tools for
• building data warehouses from structured data
• integrating structured heterogeneous databases
• mining structured data
• forming some links and associations
• information retrieval tools
• image processing and analysis
• pattern recognition
• video information processing
• visualizing data
• managing metadata

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What are our challenges?

• Do the tools scale for large heterogeneous
  databases and petabyte sized databases?
• Building models in real-time need training data
• Extracting metadata from unstructured data
• Mining unstructured data
• Extracting useful patterns from
  knowledge-directed data mining
• Rapidly forming links and associations get the
  big picture for real-time data mining
• Detecting/preventing cyber attacks
• Mining the web
• Evaluating data mining algorithms
• Conducting risks analysis / economic impact
• Building testbeds

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IN SUMMARY

• Data Mining is very useful to solve Security
  Problems
• Data mining tools could be used to examine audit
  data and flag abnormal behavior
• Much recent work in Intrusion detection (unit
  18)
• e.g., Neural networks to detect abnormal patterns
• Tools are being examined to determine abnormal
  patterns for national security
• Classification techniques, Link analysis
• Fraud detection
• Credit cards, calling cards, identity theft etc.
• BUT CONCERNS FOR PRIVACY

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Outline

• Data Mining and Privacy - Review
• Some Aspects of Privacy
• Privacy Preserving Data Mining
• Platform for Privacy Preferences
• Challenges and Discussion

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Some Privacy concerns

• Medical and Healthcare
• Employers, marketers, or others knowing of
  private medical concerns
• Security
• Allowing access to individuals travel and
  spending data
• Allowing access to web surfing behavior
• Marketing, Sales, and Finance
• Allowing access to individuals purchases

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Data Mining as a Threat to Privacy

• Data mining gives us facts that are not obvious
  to human analysts of the data
• Can general trends across individuals be
  determined without revealing information about
  individuals?
• Possible threats
• Combine collections of data and infer information
  that is private
• Disease information from prescription data
• Military Action from Pizza delivery to pentagon
• Need to protect the associations and correlations
  between the data that are sensitive or private

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Some Privacy Problems and Potential Solutions

• Problem Privacy violations that result due to
  data mining
• Potential solution Privacy-preserving data
  mining
• Problem Privacy violations that result due to
  the Inference problem
• Inference is the process of deducing sensitive
  information from the legitimate responses
  received to user queries
• Potential solution Privacy Constraint Processing
• Problem Privacy violations due to un-encrypted
  data
• Potential solution Encryption at different
  levels
• Problem Privacy violation due to poor system
  design
• Potential solution Develop methodology for
  designing privacy-enhanced systems

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Some DirectionsPrivacy Preserving Data Mining

• Prevent useful results from mining
• Introduce cover stories to give false results
  
• Only make a sample of data available so that an
  adversary is unable to come up with useful rules
  and predictive functions
• Randomization
• Introduce random values into the data and/or
  results
• Challenge is to introduce random values without
  significantly affecting the data mining results
• Give range of values for results instead of exact
  values
• Secure Multi-party Computation
• Each party knows its own inputs encryption
  techniques used to compute final results
• Rules, predictive functions
• Approach Only make a sample of data available
• Limits ability to learn good classifier

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Privacy Preserving Data MiningAgrawal and
Srikant (IBM)

• Value Distortion
• Introduce a value Xi r instead of Xi where r is
  a random value drawn from some distribution
• Uniform, Gaussian
• Quantifying privacy
• Introduce a measure based on how closely the
  original values of modified attribute can be
  estimated
• Challenge is to develop appropriate models
• Develop training set based on perturbed data
• Evolved from inference problem in statistical
  databases

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Privacy Constraint Processing

• Privacy constraints processing
• Based on prior research in security constraint
  processing
• Simple Constraint an attribute of a document is
  private
• Content-based constraint If document contains
  information about X, then it is private
• Association-based Constraint Two or more
  documents taken together is private individually
  each document is public
• Release constraint After X is released Y becomes
  private
• Augment a database system with a privacy
  controller for constraint processing

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Architecture for Privacy Constraint Processing
User Interface Manager
Privacy Constraints
Constraint Manager
Database Design Tool Constraints during database
design operation
Update Processor Constraints during update
operation
Query Processor Constraints during query and
release operations
DBMS
Database
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Semantic Model for Privacy Control
Dark lines/boxes contain private information
Cancer
Influenza
Has disease
Johns address
Patient John
England
address
Travels frequently
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Data Mining and Privacy Friends or Foes?

• They are neither friends nor foes
• Need advances in both data mining and privacy
• Need to design flexible systems
• For some applications one may have to focus
  entirely on pure data mining while for some
  others there may be a need for privacy-preserving
  data mining
• Need flexible data mining techniques that can
  adapt to the changing environments
• Technologists, legal specialists, social
  scientists, policy makers and privacy advocates
  MUST work together

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Platform for Privacy Preferences (P3P) What is
it?

• P3P is an emerging industry standard that enables
  web sites t9o express their privacy practices in
  a standard format
• The format of the policies can be automatically
  retrieved and understood by user agents
• It is a product of W3C World wide web consortium
• www.w3c.org
• Main difference between privacy and security
• User is informed of the privacy policies
• User is not informed of the security policies

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Platform for Privacy Preferences (P3P) Key
Points

• When a user enters a web site, the privacy
  policies of the web site is conveyed to the user
• If the privacy policies are different from user
  preferences, the user is notified
• User can then decide how to proceed

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Platform for Privacy Preferences (P3P)
Organizations

• Several major corporations are working on P3P
  standards including
• Microsoft
• IBM
• HP
• NEC
• Nokia
• NCR
• Web sites have also implemented P3P
• Semantic web group has adopted P3P

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Platform for Privacy Preferences (P3P)
Specifications

• Initial version of P3P used RDF to specify
  policies
• Recent version has migrated to XML
• P3P Policies use XML with namespaces for
  encoding policies
• Example Catalog shopping
• Your name will not be given to a third party but
  your purchases will be given to a third party
• ltPOLICIES xmlns http//www.w3.org/2002/01/P3Pv1gt
  
• ltPOLICY name - - - -
• lt/POLICYgt
• lt/POLICIESgt

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Platform for Privacy Preferences (P3P)
Specifications (Concluded)

• P3P has its own statements a d data types
  expressed in XML
• P3P schemas utilize XML schemas
• XML is a prerequisite to understanding P3P
• P3P specification released in January 20005 uses
  catalog shopping example to explain concepts
• P3P is an International standard and is an
  ongoing project

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P3P and Legal Issues

• P3P does not replace laws
• P3P work together with the law
• What happens if the web sites do no honor their
  P3P policies
• Then appropriate legal actions will have to be
  taken
• XML is the technology to specify P3P policies
• Policy experts will have to specify the policies
• Technologies will have to develop the
  specifications
• Legal experts will have to take actions if the
  policies are violated

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Challenges and Discussion

• Technology alone is not sufficient for privacy
• We need technologists, Policy expert, Legal
  experts and Social scientists to work on Privacy
• Some well known people have said Forget about
  privacy
• Should we pursue working on Privacy?
• Interesting research problems
• Interdisciplinary research
• Something is better than nothing
• Try to prevent privacy violations
• If violations occur then prosecute
• Discussion?