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*** This repository is the implementation of the following papers: ***

  1. 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, Hangzhou, China, September 2014

  2. Hien To, Gabriel Ghinita, and Cyrus Shahabi , PrivGeoCrowd: A Toolbox for Studying Private Spatial Crowdsourcing, 2015 IEEE 31st International Conference on Data Engineering (ICDE), (demonstration), Page 1404 - 1407, Korea, 13-17 April 2015

Related studies:

https://github.com/ubriela/geocrowd-priv-dynamic

https://github.com/ubriela/geocrowd-priv-demo

https://github.com/infolab-usc/geocrowd

https://github.com/infolab-usc/SCAWG

https://github.com/ubriela/geocrowd-complex

----------- VERSION ---------------------------------

1.1

----------- REQUIRED LIBRARIES ----------------------

numpy

----------- PACKAGES --------------------------------

exp
    : experimentation
common
    common classes
geocast
    : geocast classes
grid
    : grid-based PSDs
htree
    : htree-base PSDs
idce12
    : quadtree-based and kdtree-based PSDs
localness
    : localness approaches
tornado
    : a package to provide services for VLDB14's demo
minball
    : library to find smallest enclosing circle
log
    : PSD logs and geocast logs

----------- USAGES -----------------------------------

A) PSD Experiments

Parameters in htree/Params.py

DATASET: name of the dataset, e.g., "yelp"
+ PercentSplit: budget allocated for split
+ splitFrac: splitting budget percentage for 1st level, (1-splitFrac) for 2nd level
+ minPartSizeHTree: maximum number of data points in a leaf node
+ dynamicGranularity: compute granularity of the htree automatically
+ c_htree: a constant for computing granularity automatically
+ CONSTRAINT_INFERENCE: applying constraint inference (for both htrees and grids)
+ partitionsHTree: maximum size of the htree
+ switchPartitionsHTree: determine the size of first level tree (used for both h-trees and grids)
+ nQuery: the number of queries
+ queryUnit: the maximum query size
+ IS_LOGGING: enable logging individual query result

Parameters in PSDExp.py (starting point is at the end) seed_list: an array of seeds shape_list: an array of query size (the maximum query size is queryUnit in htree/Params.py) eps_list: an array of budgets

Run experiments with the following command

python PSDExp.py

The aggregated estimation error is in the corresponding folder in ../output/ (see the PSD RESULT)

The core dump estimation error is in folder ../log/. Each file is associated with a method. (see the CORE DUMP)

The debug file is in exp/debug.log

B) Geocast Experiments

python GeocastExp.py

----------- PSD RESULT --------------------------------------

Each file contains aggregated results of nQuery*size(seed_list) queries.

Data in each file is in tabular format. Rows are corresponding to budgets (i.e., eps_list) while columns are corresponding to PSD variants (method_list in PSDExp.py)

----------- PSD CORE DUMP -----------------------------------

Each file in ../log/ dumps the following information: query coordinates, query area (square km), values, errors

Format

minLat    minLng    maxLat    maxLng    area    real_val    est_val    abs_err    rel_err

----------- GEOCAST CORE DUMP -------------------------------

File name: geocast_{eps}.log
Each line format: <is_assigned, #cells, lat lon, cell_1 info, cell_2 info....>
1, 1, -120.081499 47.863793, 1 4 4 33 678 674.4 3.2 1.0 1.0
1, 2, -108.865274 32.231816, 3 0 23 9 75 47.1 2.1 0.906 0.906, 3 0 23 8 154 190.7 2.9 1.0 1.0

Info of each cell includes: <k l i j  #workers noisy_count cost u_c u  >
    >> (k, l): index of the first level grid
    >> (i, j): index of the second level grid (in case level = 2), i,j=1 means this is the first level cell
    >> cost: distance between the task and the cell's center
    >> u_c: utility of the cell
    >> u: updated utility