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01000naa a22002652 4500 |
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NLM117122785 |
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DE-627 |
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20231222175830.0 |
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tu |
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231222s2001 xx ||||| 00| ||eng c |
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5 |
2 |
|a pubmed24n0391.xml
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|a (DE-627)NLM117122785
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|a (NLM)11825171
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Dreiseitl, S
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Disambiguation data
|b extracting information from anonymized sources
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| 264 |
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1 |
|c 2001
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 24.05.2002
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| 500 |
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|a Date Revised 13.11.2018
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| 500 |
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a Privacy protection is an important consideration when releasing medical databases to the research community. We show that while recent advances in anonymization algorithms provide increased levels of protection, it is still possible to calculate approximations to the original data set. In some cases, one can even uniquely reconstruct entries in a table before anonymization. In this paper, we demonstrate how knowledge of an anonymization algorithm based on ambiguating data cell entries can be used to undo the anonymization process. We investigate the effect of this algorithm and its reversal on data sets of varying sizes and distributions. It is shown that by using a computationally complex disambiguation process, information on individuals can be extracted from an anonymized data set
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Vinterbo, S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ohno-Machado, L
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Proceedings. AMIA Symposium
|d 1998
|g (2001) vom: 11., Seite 144-8
|w (DE-627)NLM098642928
|x 1531-605X
|7 nnns
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| 773 |
1 |
8 |
|g year:2001
|g day:11
|g pages:144-8
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| 912 |
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|a GBV_USEFLAG_A
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| 912 |
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|a SYSFLAG_A
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| 912 |
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|a GBV_NLM
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| 912 |
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|a GBV_ILN_350
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| 951 |
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|a AR
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| 952 |
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|j 2001
|b 11
|h 144-8
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