Entry Ji:2016:GGD from tissec.bib
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BibTeX entry
@Article{Ji:2016:GGD,
author = "Shouling Ji and Weiqing Li and Mudhakar Srivatsa and
Jing Selena He and Raheem Beyah",
title = "General Graph Data De-Anonymization: From Mobility
Traces to Social Networks",
journal = j-TISSEC,
volume = "18",
number = "4",
pages = "12:1--12:??",
month = may,
year = "2016",
CODEN = "ATISBQ",
DOI = "https://doi.org/10.1145/2894760",
ISSN = "1094-9224 (print), 1557-7406 (electronic)",
ISSN-L = "1094-9224",
bibdate = "Sat May 21 08:19:26 MDT 2016",
bibsource = "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tissec.bib",
abstract = "When people utilize social applications and services,
their privacy suffers a potential serious threat. In
this article, we present a novel, robust, and effective
de-anonymization attack to mobility trace data and
social data. First, we design a Unified Similarity (US)
measurement, which takes account of local and global
structural characteristics of data, information
obtained from auxiliary data, and knowledge inherited
from ongoing de-anonymization results. By analyzing the
measurement on real datasets, we find that some data
can potentially be de-anonymized accurately and the
other can be de-anonymized in a coarse granularity.
Utilizing this property, we present a US-based
De-Anonymization (DA) framework, which iteratively
de-anonymizes data with accuracy guarantee. Then, to
de-anonymize large-scale data without knowledge of the
overlap size between the anonymized data and the
auxiliary data, we generalize DA to an Adaptive
De-Anonymization (ADA) framework. By smartly working on
two core matching subgraphs, ADA achieves high
de-anonymization accuracy and reduces computational
overhead. Finally, we examine the presented
de-anonymization attack on three well-known mobility
traces: St Andrews, Infocom06, and Smallblue, and three
social datasets: ArnetMiner, Google+, and Facebook. The
experimental results demonstrate that the presented
de-anonymization framework is very effective and robust
to noise. The source code and employed datasets are now
publicly available at SecGraph [2015].",
acknowledgement = ack-nhfb,
articleno = "12",
fjournal = "ACM Transactions on Information and System Security",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J789",
}
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- robust,
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- scale, large-,
2(2)138,
10(3)11,
16(3)11,
17(4)13,
17(4)16
- serious,
16(3)9
- service,
2(4)354,
4(4)453,
6(4)472,
10(1)2,
10(2)8,
10(3)11,
10(4)4,
10(4)6,
11(1)2,
11(1)4,
11(3)15,
11(4)19,
12(2)12,
12(3)16,
13(4)38,
13(4)39,
13(4)41,
14(1)5,
14(3)23,
15(2)6,
15(3)12,
15(3)13,
16(2)5,
16(2)6,
16(3)11,
16(3)12,
16(4)14,
17(4)13,
17(4)15,
18(1)1,
18(1)2,
18(4)13
- similarity,
2(3)295,
10(1)3,
16(4)16
- size,
2(3)295,
10(3)11,
10(4)5,
11(2)6,
11(3)13,
11(4)18,
12(3)16,
12(4)20,
14(1)3,
14(4)29,
17(4)15,
18(1)4
- social,
13(1)6
- source,
1(1)66,
5(3)238,
12(2)11,
12(2)13,
13(1)8,
13(3)21,
14(1)8,
14(3)23,
14(3)25,
15(2)6,
15(3)13
- structural,
13(4)36
- suffer,
10(4)4,
15(1)4,
16(2)7,
17(1)2
- take,
2(4)416,
10(4)1,
12(3)17,
12(3)19,
12(4)22,
13(3)20,
16(2)5,
16(3)9,
16(3)10,
16(4)15,
17(3)10,
18(1)4,
18(2)5
- then,
1(1)3,
2(1)3,
2(1)65,
2(2)138,
9(4)461,
10(1)2,
10(1)4,
11(2)4,
11(4)18,
12(2)8,
12(2)13,
12(3)18,
12(4)22,
13(1)10,
13(3)22,
13(3)25,
13(4)32,
14(4)30,
14(4)31,
14(4)32,
15(1)4,
15(2)6,
15(3)13,
15(4)18,
16(2)5,
16(2)8,
17(3)9,
17(4)15,
18(1)3,
18(1)4
- threat,
7(4)489,
10(3)11,
11(2)2,
11(2)3,
12(2)12,
14(1)7,
14(3)24,
15(1)2,
15(2)7,
16(2)6,
16(2)8,
16(3)9,
17(4)16,
18(2)5,
18(2)7,
18(4)14
- three,
2(1)105,
2(3)332,
9(2)181,
12(2)10,
13(3)25,
13(4)31,
16(2)6,
16(2)8,
16(4)16,
17(2)8,
17(4)13,
18(3)11
- trace,
13(2)18,
14(1)6,
15(3)13,
18(1)4
- two,
1(1)26,
2(4)416,
9(4)391,
10(1)4,
10(2)6,
10(2)8,
10(3)10,
10(4)4,
11(2)1,
11(2)4,
11(2)6,
11(3)13,
11(4)22,
12(1)2,
12(1)3,
12(1)4,
12(1)6,
12(3)14,
12(4)20,
13(3)22,
13(3)27,
13(4)40,
14(1)4,
14(1)5,
14(4)30,
15(1)2,
15(1)5,
15(2)6,
15(3)11,
15(3)12,
16(1)1,
16(1)2,
16(1)4,
16(3)9,
16(3)10,
16(4)15,
17(4)13,
18(2)5,
18(4)13,
18(4)14
- utilize,
10(1)3,
10(4)1,
11(4)19,
12(2)13,
18(3)11
- utilizing,
11(4)19,
16(3)11
- very,
1(1)3,
2(1)65,
2(3)269,
10(4)1,
11(2)5,
11(3)14,
11(4)18,
13(3)22,
13(3)27,
14(1)2,
15(1)2,
15(3)12,
16(1)1,
16(3)11,
16(4)13,
17(3)10,
17(4)15,
17(4)16,
18(2)7,
18(3)11,
18(4)14
- well-known,
2(1)65,
2(2)138,
12(1)5,
13(3)24,
15(3)13,
16(1)2
- when,
2(3)295,
10(2)7,
10(3)12,
10(4)4,
10(4)6,
11(2)3,
11(2)6,
11(3)15,
11(3)16,
11(4)17,
11(4)18,
12(2)10,
12(2)11,
12(4)20,
12(4)22,
13(1)10,
13(3)27,
13(4)32,
13(4)34,
13(4)35,
13(4)37,
15(2)6,
15(2)9,
15(2)10,
15(3)14,
16(1)3,
16(2)8,
17(3)9,
17(4)13,
18(3)9