Assessing the Trustworthiness of Location Data Based on Provenance

1. Assessing the Trustworthiness of Location Data Based on Provenance Chenyun Dai, Hyo-Sang Lim, Elisa Bertino Purdue University Yang-SaeMoon Kangwon National University

2. Outline • Motivation • Definitions • Computing Trustworthiness • Collusion • Performance Study • Conclusion

3. Motivation • Forensics analysis and disease control • Locations of individuals (e.g., a suspect was present at the scene of a crime) • Individuals may lie or information may not be precise • Mobile computing techniques (GPS, cell phone) • Approximate information or stolen

4. Motivation • An example • Peter’s location • Chicago, 5pm -> Lafayette, 8pm -> Cincinati, 10pm (reported by a GPS service) • Los Angels, 5pm ->San Francisco, 8pm -> Seattle, 10pm (reported by a cell phone service) • Lafayette, 8pm (reported by the local police.) • Two events are most likely possible: a) Peter was at Lafayette at 8pm; b) Peter was at Seattle at 8pm.

5. Motivation • Trustworthiness of location information depends on the source • E.g., Cell phone logs are transmitted through the GSM network • Evidences forward/process through investigation agencies • How much we can trust the evidence items? • How much we can trust the source?

6. Motivation • Problems: • Do the evidence items reported by one source support each other? • Do the trajectories reported by different sources about an individual support each other? • Where does the evidence items come from? • How to detect potential collusion among different sources and agents?

7. Definitions • Time-Stamped Position and Trajectory • A timestamped position P is (oid, s, t), oid is a unique identifier, s is a two-dimensional point (x,y) in S, and t is a time-stamp in Z. A trajectory T is a sequence of time-stamped positions P0 = (oid, s0, t0), …, Pk = (oid, sk, tk) where 0<=i<k (ti < ti+1). • Movement • Given a trajectory T = (P0, … ,Pk), a movement M is a pair of a position and its successor, that is (Pi, Pi+1). We call Pi and Pi+1 the start and end positions of M. i+1 i+1 i+1

8. Definitions • Group of Movements • Let M be a movement, and let M.Ps and M.Pe denote its start and end positions. Given a distance threshold, a group of movements G is a set of movements such that:

9. Definitions • Support and Conflict • Let T and T’ be two trajectories of the same object. Pa = (oid, sa, ta) be a position in T , Pb = (oid, sb, tb) and Pc = (oid, sc, tc) be two positions in T’ such that tb < ta < tc, and fline (.,.) be an interpolation function. Given a distance threshold , if D(Pa, fline (Pb,Pc)) <, we say that Pa and (Pb,Pc) support each other; otherwise Pa conflicts with (Pb,Pc).

10. Definitions • Provenance • For a position P, let S be its source provider and I1,…, Im be m intermediate agents that processed P. The provenance of P is a sequence of S, I1, …, Im which describes the flow of the data according to time-based ordering.

11. An example of definitions

12. Computing Trustworthiness • Intra-assessment • Clustering Positions • Grouping Movements • Assigning the Trust Scores

13. Grouping Movements • A group of movement: two positions are in the same cluster and their successor are in one cluster

14. Assigning Trust Scores • Normalized distance • Trust score

15. Inter-Assessment • P have k different (linearly interpolated) positions P1,...,Pk of k different trajectories at the time-stamp of P. • Count the number of supports and the number of conflicts • Scoreinter (P) as follows:

16. Provenance-based assessment • Trust Scores of Positions • Trust Scores of Network Nodes

17. Integration of trust scores

18. Collusion • A collusion attack • An active form of lies involving two or more subjects who provide evidences • Frequently by a group of people who want to make a fake evidence • Avoid possible disadvantages over the group due to a true event

19. Collusion • Majority Rule • Let C0,…, Ci be clusters of positions which represent evidences for an object at a certain time. If the average trust score of Ck is larger than the average trust scores of any other clusters, we call positions in Ck are correct evidences for an event of O at t, and the positions in the other clusters are incorrect evidences. Ck

20. Reflecting Collusion • Observation 1. The less the number of network nodes involved with a position cluster of incorrect evidences, the more penalty for the possible collusion.

21. Reflecting Collusion • Observation 2. The more simila provenance among positions in a cluster of incorrect evidences, the more penalty for the possible collusion.

22. Reflecting Collusion • Observation 3. The more the number of positions in a cluster of incorrect evidences, the more penalty for the possible collusion.

23. Performance Study • Intra-source assessment consumes most of the time. • Inter-source assessment grows almost linear to the data size. our algorithm is linear to the number of trajectories. • The time on provenance-based assessment is very small and grows linearly.

24. Performance Study • The trust score remains high for correct data and remains low for incorrect data. • The trust scores of both inter and intra assessment in two figures remain almost unchanged

25. Performance Study • The intra-assessment trust score increases slightly. The reason is that the randomness of the incorrect data increases the deviation of the distribution. • Both assessments remain unchanged. We only introduced random incorrect data

26. Performance Study • Collusions affect the trust scores of both correct and incorrect data • Trust score of inter-assessment increases when the ratio of incorrect data increases.

27. Performance Study

28. Conclusion • Importance of considering the trustworthiness of location • Defined the problem based on some simple spatio-temporal notions • proposed a systematic method for assessing the trust scores of location data provided by multiple sources. • Handle collusion attacks

29. Future work • The privacy of location data • Locations with different spatial granularities • More accurate collusion detection