Research Article
Support Personalized Weighted Local Differential Privacy Skyline Query
Algorithm 3
Skyline query based on PWLDP algorithm.
| Input: : local skyline query results for each organization; : privacy budget; : the proportion of weights under each attribute. | | Output: = : global skyline query results. | (1) | for local skyline query data set from 1 to do | (2) | for data record from 1 to do | (3) | for each attribute from 1 to do | (4) | | (5) | | (6) | | (7) | | (8) | = np.full(shape = , fill_value = ) | (9) | | (10) | = np.random.choice(a = range(1, ), p = | (11) | end for | (12) | end for | (13) | end for | (14) | for each in List do | (15) | flag = True | (16) | for each in List do | (17) | ifthen | (18) | if () then | (19) | return or | (20) | flag = | (21) | end if | (22) | end if | (23) | end for | (24) | return. Index | (25) | end for | (26) | | (27) | return to each organization |
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