A General Proximity Privacy Principle
This work presents a systematic study of the problem of protecting general proximity privacy, with findings applicable to most existing data models. Our contributions are multi-folded: we highlighted and formulated proximity privacy breaches in a data-model-neutral manner; we proposed a new privacy...
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| creator | Ting Wang Shicong Meng Bamba, B. Ling Liu Pu, C. |
| description | This work presents a systematic study of the problem of protecting general proximity privacy, with findings applicable to most existing data models. Our contributions are multi-folded: we highlighted and formulated proximity privacy breaches in a data-model-neutral manner; we proposed a new privacy principle (epsiv,delta) k -dissimilarity, with theoretically guaranteed protection against linking attacks in terms of both exact and proximate QI-SA associations; we provided a theoretical analysis regarding the satisfiability of (epsiv,delta) k -dissimilarity, and pointed to promising solutions to fulfilling this principle. |
| doi_str_mv | 10.1109/ICDE.2009.220 |
| format | Conference Proceeding |
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| identifier | ISSN: 1063-6382 |
| ispartof | 2009 IEEE 25th International Conference on Data Engineering, 2009, p.1279-1282 |
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| language | eng |
| recordid | cdi_ieee_primary_4812520 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Data engineering Data privacy Data security Diseases Educational institutions Information security Joining processes Protection Publishing Uncertainty |
| title | A General Proximity Privacy Principle |
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