Research Article

A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing

Table 2

Three anonymous examples.

Class IDTimeLocationAnonymized timeAnonymized location

120:47:36(21.3675, −157.9388)20:38:22(21.3166, −157.8616)
121:46:33(21.2866, −157.8129)20:38:22(21.3166, −157.8616)
119:20:57(21.2958, −157.8331)20:38:22(21.3166, −157.8616)
223:31:00(45.5894, −122.7524)23:00:57(46.3272, −122.5448)
222:00:00(45.7801, −122.5400)23:00:57(46.3272, −122.5448)
223:31:50(47.6122, −122.3419)23:00:57(46.3272, −122.5448)
318:54:05(30.4810, −97.8295)19:15:09(32.0273, −97.4996)
319:33:26(32.7368, −97.3271)19:15:09(32.0273, −97.4996)
319:17:48(32.8640, −97.3421)19:15:09(32.0273, −97.4996)
418:46:48(30.2016, −97.6671)19:02:11(31.5155, −97.4498)
419:09:06(32.6804, −97.3746)19:02:11(31.5155, −97.4498)
419:03:47(32.8382, −97.0045)19:02:11(31.5155, −97.4498)
419:09:03(30.3417, −97.7530)19:02:11(31.5155, −97.4498)
519:50:21(59.3238, 18.0977)17:25:48(59.3232, 18.0543)
515:49:08(59.3457, 18.0587)17:25:48(59.3232, 18.0543)
516:38:04(59.3055, 17.9892)17:25:48(59.3232, 18.0543)
517:28:22(59.3122, 18.0796)17:25:48(59.3232, 18.0543)
517:23:05(59.3288, 18.0461)17:25:48(59.3232, 18.0543)