Name
Abstract | ||
|---|---|---|
We identify proximity breach as a privacy threat specific to numerical sensitive attributes in anonymized data publication. Such breach occurs when an adversary concludes with high confidence that the sensitive value of a victim individual must fall in a short interval --- even though the adversary may have low confidence about the victim's actual value. None of the existing anonymization principles (e.g., k-anonymity, l-diversity, etc.) can effectively prevent proximity breach. We remedy the problem by introducing a novel principle called (ε, m)-anonymity. Intuitively, the principle demands that, given a QI-group G, for every sensitive value x in G, at most 1/m of the tuples in G can have sensitive values "similar" to x, where the similarity is controlled by ε. We provide a careful analytical study of the theoretical characteristics of (ε, m)-anonymity, and the corresponding generalization algorithm. Our findings are verified by experiments with real data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1376616.1376666 | SIGMOD Conference |
Keywords | Field | DocType |
anonymized data publication,proximity privacy,high confidence,existing anonymization principle,numerical sensitive data,novel principle,actual value,numerical sensitive attribute,sensitive value,low confidence,qi-group g,proximity breach,generic algorithm,privacy | Low Confidence,Data mining,Computer science,Tuple,Computer security,k-anonymity,Anonymity,Publishing,Adversary,Database | Conference |
Citations | PageRank | References |
49 | 1.44 | 40 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiexing Li | 1 | 211 | 10.36 |
| Yufei Tao | 2 | 7231 | 316.71 |
| Xiaokui Xiao | 3 | 3266 | 142.32 |