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Data%20and%20Applications%20Security%20Developments%20and%20Directions

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Title: Data%20and%20Applications%20Security%20Developments%20and%20Directions


1
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

2
Objective of the Unit
  • This unit provides an overview of data mining for
    security (national security) and then discusses
    privacy

3
Data Mining for Counter-terrorism
4
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

5
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
6
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

7
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
8
Data Mining Outcomes and Techniques for
Counter-terrorism
9
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

10
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

11
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

12
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

13
Outline
  • Data Mining and Privacy - Review
  • Some Aspects of Privacy
  • Privacy Preserving Data Mining
  • Platform for Privacy Preferences
  • Challenges and Discussion

14
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

15
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

16
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

17
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

18
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

19
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

20
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
21
Semantic Model for Privacy Control
Dark lines/boxes contain private information
Cancer
Influenza
Has disease
Johns address
Patient John
England
address
Travels frequently
22
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

23
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

24
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

25
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

26
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

27
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

28
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

29
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?
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