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Preserving User Location Privacy in Mobile Data Management Infrastructures

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Location Privacy (PET'06) Cheng, Zhang, Bertino, Prabhakar. 3. The Location Privacy Problem ... Location Privacy (PET'06) Cheng, Zhang, Bertino, Prabhakar. 11 ... – PowerPoint PPT presentation

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Title: Preserving User Location Privacy in Mobile Data Management Infrastructures


1
Preserving User Location Privacy in Mobile Data
Management Infrastructures
The 6th Workshop on Privacy Enhancing
Technologies (2006)
  • Reynold Cheng (csckcheng_at_comp.polyu.edu.hk)
  • The Hong Kong Polytechnic University

A joint work with Yu Zhang, Elisa Bertino, and
Sunil Prabhakar Purdue University
2
Location-Based Services
Find a friend within 50m of my location.
Where is my nearest gas station?
Service Provider
3
The Location Privacy Problem
  • Beresford et al. BG03 Location privacy is the
    ability to prevent other parties from learning
    ones current or past location
  • Need to prevent
  • Tracking of the users whereabouts
  • Discovery of the users personal habits

4
Location Cloaking BS03,GG03
Actual Location
y
x
time
Uncertainty region seen by service provider
5
Privacy, Cloaking, and Quality
Location Cloaking
More uncertainty, More privacy
More uncertainty, Poorer service
Better service, lower privacy?
Location Privacy
Service Quality
6
Location Cloaking Framework CP04
Imprecise Location Service Request
Precise Location Service Request
Location Cloaking Engine
Service Provider
User
Service quality report
7
Our Contributions
  • A framework that trades off location privacy and
    service quality
  • An efficient algorithm for processing an
    important query class
  • Definition of query quality
  • Experimental simulations
  • Privacy threats and solutions

8
Cloaked Location Model
Uniform distribution
Evaluated by imprecise queries to produce answers
with probabilistic confidence
9
The Cloaking Agent
10
The Policy Translator
  • Possible privacy preferences
  • k-anonymity BG03, GG03 at least k users in the
    cloaking region
  • Privacy minimum uncertainty region size
  • Accuracy maximum uncertainty region size
  • Locations cloaking required when being near to a
    certain object (physical or logical)
  • Other users/service providers presence
    known/hidden to them?

11
Service Translator and Service Provider
  • Evaluate cloaked data, provide probabilistic
    answer, compute quality
  • Example Range query (e.g., who is within 50m
    from me)

12
Result Translator
  • Provide query result and quality reports
  • Convert probabilistic answers to interpretable
    results
  • Example Map probability ranges
    (0,0.2,(0.2,0.8,(0.8,1 to LOW, MEDIUM and HIGH

13
Precise Location-based Range Query
Example Who is within 100 metres from me?
Only S4 is the answer.
14
Imprecise Location-based Range Query
Overall probability (S2,0.1),, (S3,0.7),
(S4,0.9)
Q2 (S3,0.9), (S4,1)
Q1 (S2,0.2),, (S3,0.6), (S4,0.7)
15
Query Evaluation (1)
  • Transformation decomposes imprecise queries into
    sub-queries
  • Evaluation computes the probabilistic answers
    for each precise sub-queries
  • Aggregation summarizes the final result from all
    sub-queries

16
Query Evaluation (2)
  • Probability pj(u,v) of user Sj satisfying the
    range query issued at point (u,v) ? U

Can be Expensive!
  • Probability pj of user Sj satisfying the range
    query issued by U

17
Efficient Query Evaluation
  • Pruning removes all objects that do not have any
    chance of satisfying the query
  • Transformation decomposes imprecise queries into
    sub-queries
  • Evaluation computes the probabilistic answers
    for each precise sub-queries
  • Aggregation summarizes the final result from all
    sub-queries

18
Pruning Cloaked Locations
  • The Minkowski Sum can be evaluated with
    computational geometry techniques BK00

19
Quality of Imprecise Queries
  • Query quality metric measures the effect of
    cloaking on service quality
  • Query quality is affected by
  • Uncertainty of query issuers location
  • Uncertainty of data being queried

20
Quality of Imprecise Queries
  • The larger the query issuers uncertainty, the
    more likely that different sub-query answers are
    generated
  • Low quality when
  • There are many different answer sets
  • The members of different answer sets differ from
    each other significantly

21
Query Quality An Illustration
22
Query Quality Metric
  • Precision of Rk with respect to R
  • Probability that S gets the answer Rk
  • Query Score

23
Experiment Model
  • Based on the City Simulator 2.0 developed at IBM
    KMJ01
  • 71 buildings, 48 roads, 6 road intersections and
    1 park
  • 10,000 people moving in a city

24
Quality and Privacy
25
Privacy and Performance
26
Quality and Query Size
27
Implementation Issues
  • Systems that dont track locations regularly
  • Example GPS, RFID
  • GPS receiver in user obtains info from satellites
  • Cloaking agent controls when to report location
  • Systems that track locations regularly
  • Example GSM, PCS
  • Cloaking agent reports cloaked locations in terms
    of neighboring cells regularly WL00

28
References
  • BK00 M. Berg, M. Kreveld, M. Overmars and O.
    Schwarzkopf. Computational Geometry Algorithms
    and Applications. 2nd ed., Springer Verlag
    (2000).
  • BS03 A. Beresford and F. Stajano. Location
    Privacy in Pervasive Computing. IEEE Pervasive
    Computing, 2(1)46-55, 2003.
  • CKP03 R. Cheng, D. Kalashnikov and S.
    Prabhakar. Evaluating Probabilistic Queries over
    Imprecise Data. In Proc. of ACM SIGMOD, June
    2003.
  • CKP04 R. Cheng, D. Kalashnikov and S.
    Prabhakar. Querying Imprecise Data in Moving
    Object Environments. . In Transactions of
    Knowledge and Data Engineering, 2004.
  • CP04 R. Cheng and S. Prabhakar. Using
    uncertainty to provide privacy-preserving and
    high-quality location-based services. In Workshop
    on Location Systems Privacy and Control,
    MobileHCI 2004.
  • GG03 M. Gruteser and D. Grunwald. Anonymous
    Usage of Location-based Services through Spatial
    and Temporal Cloaking. In Proc. of the 1st Intl.
    Conf. on Mobile Systems, Applications and
    Services, May 2003.
  • GL05 B. Gedik and L. Liu. Location Privacy in
    Mobile Systems A Personalized Anonymization
    Model. ICDCS, 2005.
  • KMJ01 J. Kaufman, J. Myllymaki and J. Jackson.
    IBM City Simulator Spatial Data Generator 2.0,
    2001.
  • VL2000 V. Wong and V. Leung. Location
    management for next-generation personal
    communications network. IEEE Network (2000).

29
Conclusions and Future Work
  • A framework for capturing uncertainty, location
    privacy, service quality
  • Evaluation and quality metrics for imprecise
    range queries
  • Future work
  • Large-scale data indexing
  • Other query types
  • Possible privacy threats
  • System prototype development

Contact Reynold Cheng (csckcheng_at_comp.polyu.edu.hk
) for more details http//www.comp.polyu.edu.hk/c
sckcheng
30
Related Work Cloaking
  • Adaptive-Interval Cloaking Algorithm GG03
    partition the area into quadrants of equal area
    until the user and other k-1 users are included
  • Clique-cloak algorithm GL05 each user has her
    own k-anonymity requirement
  • These work did not provide probability
    computation and precise measurements over service
    quality

31
Related Work Uncertainty Management
  • Probabilistic queries CKP03, CKP04 manage
    uncertain data in location and sensor databases
  • Evaluation of answers with probabilities
  • Metrics for query ambiguity
  • Assume queries are precise (i.e., no uncertainty
    about the query issuer)

32
Privacy of Cloaking
  • Size of uncertainty region
  • Coverage of sensitive region

33
Privacy Threats
34
Possible Solutions to Privacy Threats
35
Uncertainty vs. Velocity
36
Quality vs. Privacy
37
Response Time vs. Velocity
38
Query Pruning
  • Called the Minkowski Sum, which can be computed
    with computational geometric techniques BK00
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