Research Article
An Adaptive Grid and Incentive Mechanism for Personalized Differentially Private Location Data in the Local Setting
Algorithm 2
Hadamard count-min sketch LDP algorithm.
| Input: user’s location li, number of user n, confidence parameter , and user’s privacy specification ε1 | | Output: user location count | (1) | Server calculates the number of grid | (2) | Server calculates | (3) | Server calculates m | (4) | Server generates a random matrix | (5) | Server initializes | (6) | Server initializes z and f | (7) | for each user uido | (8) | for each hash hjdo | (9) | server randomly generates k from {1, …, m} | (10) | server sends kth row to ui | (11) | ui returns to server | (12) | server adds to kth bit of | (13) | end for | (14) | end for | (15) | for each hash hjdo | (16) | for each hashed location do | (17) | server sets ‘s ith element of c to | (18) | end for | (19) | end for | (20) | return |
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