Privacy

1
Privacy

• Prof. Bhavani Thuraisingham
• The University of Texas at Dallas
• March 5, 2008
• Lecture 18

2
What is Privacy

• Medical Community
• Privacy is about a patient determining what
  patient/medical information the doctor should be
  released about him/her
• Financial community
• A bank customer determine what financial
  information the bank should release about him/her
• Government community
• FBI would collect information about US citizens.
  However FBI determines what information about a
  US citizen it can release to say the CIA

3
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

4
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

5
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

6
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

7
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
8
Semantic Model for Privacy Control
Dark lines/boxes contain private information
Cancer
Influenza
Has disease
Johns address
Patient John
England
address
Travels frequently
9
Privacy 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

10
Cryptographic Approaches for Privacy Preserving
Data Mining

• Secure Multi-part Computation (SMC) for PPDM
• Mainly used for distributed data mining.
• Provably secure under some assumptions.
• Learned models are accurate
• Efficient/specific cryptographic solutions for
  many distributed data mining problems are
  developed.
• Mainly semi-honest assumption (i.e. parties
  follow the protocols)
• Malicious model is also explored recently. (e.g.
  Kantarcioglu and Kardes paper in this workshop)
• Many SMC based PPDM algorithms share common
  sub-protocols (e.g. dot product, summation, etc.
  )

11
Cryptographic Approaches for Privacy Preserving
Data Mining

• Drawbacks
• Still not efficient enough for very large
  datasets. (e.g. petabyte sized datasets ??)
• Semi-honest model may not be realistic
• Malicious model is even slower
• Possible new directions
• New models that can trade-off better between
  efficiency and security
• Game theoretic / incentive issues in PPDM
• Combining anonymization and cryptographic
  techniques for PPDM

12
Perturbation Based Approaches for Privacy
Preserving Data Mining

• Goal Distort data while still preserve some
  properties for data mining propose.

• Additive Based
• Multiplicative Based
• Condensation based
• Decomposition
• Data Swapping

13
Perturbation Based Approaches for Privacy
Preserving Data Mining

• Goal Achieve a high data mining accuracy with
  maximum privacy protection.

14
Perturbation Based Approaches for Privacy
Preserving Data Mining

• Privacy is a personal choice, so should enable
  individual adaptable (Liu, Kantarcioglu and
  Thuraisingham ICDM06)

15
Perturbation Based Approaches for Privacy
Preserving Data Mining

• The trend is to make PPDM approaches fit in the
  reality
• We investigated perturbation based approaches
  with real-world data sets
• We give a applicability study to the current
  approaches
• Liu, Kantarcioglu and Thuraisingham, DKE 07
• We found out,
• The reconstruction the original distribution may
  not work well with real-world data set
• Distribution is a hard problem, should not use as
  a media step
• Try to modify perturbation techniques, and adapt
  some data mining tools, e.g. Liu, Kantarcioglu
  and Thuraisingham, Novel decision tree UTD
  technical report 06

16
CPT Confidentiality, Privacy and Trust

• Before I as a user of Organization A send data
  about me to organization B, I read the privacy
  policies enforced by organization B
• If I agree to the privacy policies of
  organization B, then I will send data about me to
  organization B
• If I do not agree with the policies of
  organization B, then I can negotiate with
  organization B
• Even if the web site states that it will not
  share private information with others, do I trust
  the web site
• Note while confidentiality is enforced by the
  organization, privacy is determined by the user.
  Therefore for confidentiality, the organization
  will determine whether a user can have the data.
  If so, then the organization van further
  determine whether the user can be trusted

17
Platform for Privacy Preferences (P3P) What is
it?

• P3P is an emerging industry standard that enables
  web sites to 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
• 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
• Several major corporations are working on P3P
  standards including

18
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

19
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
• P3P has its own statements and data types
  expressed in XML P3P schemas utilize XML schemas
• P3P specification released in January 20005 uses
  catalog shopping example to explain concepts P3P
  is an International standard and is an ongoing
  project
• 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

20
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

21
Privacy for Assured Information Sharing
Data/Policy for Federation
Export
Export
Data/Policy
Data/Policy
Export
Data/Policy
Component
Component
Data/Policy for
Data/Policy for
Agency A
Agency C
Component
Data/Policy for
Agency B
22
Privacy Preserving Surveillance
Raw video surveillance data
Face Detection and Face Derecognizing system
Suspicious people found
Faces of trusted people derecognized to preserve
privacy
Suspicious events found
Comprehensive security report listing suspicious
events and people detected
Suspicious Event Detection System
Manual Inspection of video data
Report of security personnel
23
Directions Foundations of Privacy Preserving
Data Mining

• We proved in 1990 that the inference problem in
  general was unsolvable, therefore the suggestion
  was to explore the solvability aspects of the
  problem.
• Can we do something similar for privacy?
• Is the general privacy problem solvable?
• What are the complicity classes?
• What is the storage and time complicity
• We need to explore the foundation of PPDM and
  related privacy solutions

24
Directions Testbed Development and Application
Scenarios

• There are numerous PPDM related algorithms. How
  do they compare with each other? We need a
  testbed with realistic parameters to test the
  algorithms
• It is time to develop real world scenarios where
  these algorithms can be utilized
• Is it feasible to develop realistic commercial
  products or should each organization adapt
  product to suit their needs?

25
Key Points

• 1. There is no universal definition for privacy,
  each organization must definite what it means by
  privacy and develop appropriate privacy policies
• 2. Technology alone is not sufficient for privacy
  We need technologists, Policy expert, Legal
  experts and Social scientists to work on Privacy
• 3. Some well known people have said Forget about
  privacy Therefore, should we pursue research on
  Privacy?
• Interesting research problems, there need to
  continue with research
• Something is better than nothing
• Try to prevent privacy violations and if
  violations occur then prosecute
• 4. We need to tackle privacy from all directions

26
Application Specific Privacy?

• Examining privacy may make sense for healthcare
  and financial applications
• Does privacy work for Defense and Intelligence
  applications?
• 3Is it eve meaningful to have privacy for
  surveillance and geospatial applications
• Once the image of my house is on Google Earth,
  then how much privacy can I have?
• I may want my location to be private, but does it
  make sense if a camera can capture a picture of
  me?
• If there are sensors all over the place, is it
  meaningful to have privacy preserving
  surveillance?
• This suggestion that we need application specific
  privacy
• It is not meaningful to examine PPDM for every
  data mining algorithm and for every application

27
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