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1 Location Privacy. 2 Context Better localization technology + Pervasive wireless connectivity = Location-based applications.

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Presentation on theme: "1 Location Privacy. 2 Context Better localization technology + Pervasive wireless connectivity = Location-based applications."— Presentation transcript:

1 1 Location Privacy

2 2 Context Better localization technology + Pervasive wireless connectivity = Location-based applications

3 3 Location-Based Apps For Example:  GeoLife shows grocery list near WalMart  Micro-Blog allows location scoped querying  Location-based ad: Coffee coupon at Starbucks  … Location expresses context of user  Facilitating content delivery Location is the IP address Its as iffor content

4 4 While location drives this new class of applications, it also violates user’s privacy Sharper the location, richer the app, deeper the violation Double-Edged Sword

5 5 The Location Based Service Workflow Client Server LBS Database (Location Based Service) Request: Retrieve all available services in client’s location Forward to local service: Retrieve all available services in location Reply:

6 6 The Location Anonymity Problem Client Server LBS Database (Location Based Service) Request: Retrieve all bus lines from location to address == Privacy Violated

7 7 Moreover, range of apps are PUSH based. Require continuous location information Phone detected at Starbucks, PUSH a coffee coupon Phone located on highway, query traffic congestion Double-Edged Sword

8 8 Location Privacy Problem: Research: Continuous location exposure a serious threat to privacy Continuous location exposure a serious threat to privacy Preserve privacy without sacrificing the quality of continuous loc. based apps Preserve privacy without sacrificing the quality of continuous loc. based apps

9 9 Just Call Yourself ``Freddy” Pseudonymns [Gruteser04]  Effective only when infrequent location exposure  Else, spatio-temporal patterns enough to deanonymize … think breadcrumbs Romit’s Office John LeslieJack Susan Alex

10 10 A Customizable k-Anonymity Model for Protecting Location Privacy Paper by: B. Gedik, L.Liu (Georgia Tech) Slides adopted from: Tal Shoseyov

11 11 Location Anonymity “A message from a client to a database is called location anonymous if the client’s identity cannot be distinguished from other users based on the client’s location information.” Database

12 12 k-Anonymity “A message from a client to a database is called location k-anonymous if the client cannot be identified by the database based on the client’s location from other k-1 clients.”

13 13 Implementation of Location Anonymity Client sends plain request to the server Server sends “anonymized” message Database executes request according to the received anonymous data Database replies to server with compiled data Server forwards data to client Server transforms the message by “anonymizing” the location data in the message

14 14 Implementation of Location k-Anonymity Spatial Cloaking – Setting a range of space to be a single box, where all clients located within the range are said to be in the “same location”. x y Temporal Cloaking – Setting a time interval, where all the clients in a specific location sending a message in that time interval are said to have sent the message in the “same time”. t

15 15 Implementation of Location k-Anonymity x y t Spatial-Temporal Cloaking – Setting a range of space and a time interval, where all the messages sent by client inside the range in that time interval. This spatial and temporal area is called a “cloaking box”.

16 16 Previous solutions M. Gruteser, D Grunwald (2003) – For a fixed k value, the server finds the smallest area around the client’s location that potentially contains k-1 different other clients, and monitoring that area over time until such k-1 clients are found. Drawback: Fixed anonymity value for all clients (service dependent)

17 17 Add Noise K-anonymity [Gedic05]  Convert location to a space-time bounding box  Ensure K users in the box  Location Apps reply to boxed region Issues  Poor quality of location  Degrades in sparse regions  Not real-time You Bounding Box K=4

18 18 Confuse Via Mixing Path intersections is an opportunity for privacy  If users intersect in space-time, cannot say who is who later

19 19 Confuse Via Mixing Path intersections is an opportunity for privacy  If users intersect in space-time, cannot say who is who later Unfortunately, users may not intersect in both space and time Unfortunately, users may not intersect in both space and time Hospital Airport ? ?

20 20 Hiding Until Mixed Partially hide locations until users mixed [Gruteser07]  Expose after a delay Hospital Airport

21 21 Hiding Until Mixed Partially hide locations until users mixed [Gruteser07]  Expose after a delay But delays unacceptable to real-time apps Hospital Airport

22 22 Existing solutions seem to suggest: Privacy and Quality of Localization (QoL) is a zero sum game Need to sacrifice one to gain the other

23 23 Hiding Stars with Fireworks: Location Privacy through Camouflage

24 24 Goal Break away from this tradeoff Target: Spatial accuracy Real-time updates Privacy guarantees Even in sparse populations New Proposal: CacheCloak

25 25 The Intuition Predict until paths intersect Hospital Airport

26 26 The Intuition Predict until paths intersect Hospital Airport Predict

27 27 The Intuition Predict until paths intersect  Expose predicted intersection to application Hospital Airport Cache the information on each predicted location Predict

28 28 CacheCloak System Design and Evaluation

29 29 Assume trusted privacy provider  Reveal location to CacheCloak  CacheCloak exposes anonymized location to Loc. App Architecture CacheCloak Loc. App1 Loc. App2 Loc. App3 Loc. App4

30 30 In Steady State … Location Based Application CacheCloak

31 31 Prediction Location Based Application Backward prediction Forward prediction CacheCloak

32 32 Prediction Location Based Application CacheCloak

33 33 Predicted Intersection Location Based Application Predicted Path CacheCloak

34 34 Query Location Based Application Predicted Path CacheCloak

35 35 Query Location Based Application ? ?? ? CacheCloak

36 36 LBA Responds Location Based Application Array of responses CacheCloak

37 37 Cached Location Based Application Cached Responses Location based Information CacheCloak

38 38 Cached Response Location Based Application Cached Responses Location based Information CacheCloak

39 39 Cached Response Location Based Application Cached Responses Location based Information CacheCloak

40 40 Cached Response Location Based Application Cached Responses CacheCloak

41 41 Cached Response Location Based Application Predicted Path CacheCloak

42 42 Benefits Real-time  Response ready when user arrives at predicted location High QoL  Responses can be specific to location  Overhead on the wired backbone (caching helps) Entropy guarantees  Entropy increases at traffic intersections Sparse population  Can be handled with dummy users, false branching Predicted Path

43 43 Quantifying Privacy City converted into grid of small sqaures (pixels)  Users are located at a pixel at a given time Each pixel associated with 8x8 matrix  Element (x, y) = probability that user enters x and exits y Probabilities diffuse  At intersections  Over time Privacy = entropy x y pixel

44 44 Diffusion Probability of user’s presence diffuses  Diffusion gradient computed based on history  i.e., what fraction of users take right turn at this intersection Time t 1 Time t 2 Time t 3 Road Intersection

45 45 Evaluation Trace based simulation  VanetMobiSim + US Census Bureau trace data  Durham map with traffic lights, speed limits, etc.  Vehicles follow Google map paths  Performs collision avoidance 6km x 6km 10m x 10m pixel 1000 cars 6km x 6km 10m x 10m pixel 1000 cars

46 46 Results High average entropy  Quite insensitive to user density (good for sparse regions)  Minimum entropy reasonably high Number of Users (N) Time (Minutes) Min. Max. Bits of Mean Entropy

47 47 Results Peak Counting  # of places where attacker’s confidence is > Threshold Time (Seconds) Mean # of Peaks

48 48 Results Peak Counting  # of places where attacker’s confidence is > Threshold Number of Users (N) Mean # of Peaks

49 49 Limitations, Discussions … CacheCloak overhead  Application replies to lot of queries  However, overhead on wired infrastructure  Caching reduces this overhead significantly CacheCloak assumes same, indistinguishable query  Different queries can deanonymize  Possible through query combination … future work Per-user privacy guarantee not yet supported  Adaptive branching & dummy users CacheCloak - a central trusted entity  Distributed version proposed in the paper

50 50 Closing Thoughts Two nodes may intersect in space but not in time Mixing not possible, without sacrificing timeliness Mobility prediction creates space-time intersections Enables virtual mixing in future

51 51 Closing Thoughts CacheCloak Implements the prediction and caching function High entropy possible even under sparse population Spatio-temporal accuracy remains uncompromised

52 52

53 53

54 54 Thank You For more related work, visit:


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