This document discusses research on spatial crowdsourcing and differential privacy. It introduces the PriGeoCrowd framework which aims to preserve worker location privacy in spatial crowdsourcing using differential privacy. The framework uses a WorkerPSD technique to guide efficient task assignment and differentially private spatial decompositions. It also proposes a geocast query algorithm that expands the query using the WorkerPSD to navigate worker search and assignment.

1. Spatial Crowdsourcing Research
@ Infolab, USC
Hien To
hto@usc.edu

2. Scenario
2
SC-server
Worker
Requester
Scheduling First instance
Second instance

3. Spatial Crowdsourcing @ Infolab
– Kazemi ACM GIS’12: Task Assignment
– Kazemi ACM GIS’13: Trustworthiness
– Shahabi MobiDE’13 keynote
– Deng ACM GIS’13: Time constraint
– Hien To et al. iiWAS'13: Complex task
– Hien To, submitted to TSAS: Worker expertise
– Hien To: Worker location privacy

4. PriGeoCrowd: Differential Privacy Preserving in
Spatial Crowdsourcing

5. PriGeoCrowd
• Advantages
– Privacy
• Worker locations are protected with DP
• Workers can opp-in/out a geocast (WST)
– Efficiency
• SC-Server uses WorkerPSD to guide the search (SAT) 
would result in better task assignment
• Geocast query filters nearby workers of a task  reduce
communication overhead
• Focus Study
– Step 1: query answering with WorkerPSD
– Step 2: define good geocast queries

6. Step 3: WorkerPSD
• Related works
– Xiao SDM’10: equal grid
– ICDE’2012: kd-trees and quad-trees variants
– ICDE’2013: grid variants (e.g., adaptive grid (AG))
• Improvements
– Task localness: maximum threshold distance
– Utilize localness property to develop a customized
budget strategy for AG

8. Step 4: Geocast query
• Cost model for single task assignment
– Utility: probability that the task will be performed
– Cost: workers’ travel cost
2
)1(1)1(1 x
a
w
a pp 


9. Geocast algorithm
• Relax the constraints of the cost model
– Optimal result was achieved over noisy data
– Utility/travel cost functions are discrete rather
than continuous
– Utility is not manageable
– The knowledge of WorkerPSD was not used
• Geocast algorithm
– Idea: keep expanding geocast query, use
WorkerPSD to navigate the search

10. Level Expansion
10
First level
Second level

11. ANW The avg number of workers being notified
EPT The avg number of tasks being performed
DPPT The percentage of performed tasks
ATD Average travel cost per performed task

12. Other Experiments
• Compare Geocast algorithm over WorkerPSD
vs actual worker location
• Compare Geocast algorithm with using cost
model
• Compare Geocast algorithm with naïve
algorithm

13. Differentially Private Spatial
Decompositions
• ICDE’2012: kd-trees and quad-trees variants
• ICDE’2013: grid variants (e.g., adaptive grid)
• H-tree variants
– Outperform kd-tree variants
– Outperform grid variants on extremely skew
datasets (e.g., datasets with noise)

15. References
• Leyla Kazemi and Cyrus Shahabi, GeoCrowd: Enabling Query Answering with Spatial
Crowdsourcing, ACM SIGSPATIAL GIS, Redondo Beach, CA, November 2012
• Leyla Kazemi, Cyrus Shahabi, and Lei Chen, GeoTruCrowd: Trustworthy Query Answering with
Spatial Crowdsourcing, International Conference on Advances in Geographic Information Systems
(ACM SIGSPATIAL GIS 2013), Orlando, Florida , November 5-8, 2013
• Dingxiong Deng, Cyrus Shahabi, and Ugur Demiryurek, Maximizing the Number of Worker's Self-
Selected Tasks in Spatial Crowdsourcing, ACM SIGSPATIAL GIS '13, Orlando, Florida, USA, November
5-8, 2013
• Dang, Hung, Tuan Nguyen, and Hien To. Maximum Complex Task Assignment: Towards Tasks
Correlation in Spatial Crowdsourcing. In Proceedings of the 15th International Conference on
Information Integration and Web-based Applications & Services (iiWAS 2013) (Acceptance rate
~30%) (Paper) (Code)
• Hien To, Leyla Kazemi, and Cyrus Shahabi, A Server-Assigned Spatial Crowdsourcing
Framework, Journal ACM Transactions on Spatial Algorithms and Systems , Volume 1 Issue 1, Article
No. 2 (Acceptance Rate ~ 11%), New York, NY, USA, August 2015
• Hien To, Gabriel Ghinita, and Cyrus Shahabi, A Framework for Protecting Worker Location Privacy
in Spatial Crowdsourcing, In Proceedings of the 40th International Conference on Very Large Data
Bases, Pages 919-930 (Acceptance rate ~ 20%), Hangzhou, China, September 2014