Who Leaks My Privacy: Towards Automatic and Association Detection with GDPR Compliance

被引:11
作者
Jia, Qiwei [1 ]
Zhou, Lu [1 ]
Li, Huaxin [1 ]
Yang, Ruoxu [1 ]
Du, Suguo [1 ]
Zhu, Haojin [1 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
来源
WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2019 | 2019年 / 11604卷
基金
美国国家科学基金会;
关键词
GDPR; Privacy leakage; Association mining;
D O I
10.1007/978-3-030-23597-0_11
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The APPs running on smart devices have greatly enriched people's lives. However, they are collecting personally identifiable information (PII) secretly. The unrestricted collection, processing and unsafe transmission of PII will result in the disclosure of privacy, which cause losses to users. With the advent of laws and regulations about data privacy such as GDPR, the major APP vendors have become more and more cautious about collecting PII. However, the researches on detecting privacy leakage under GDPR framework still receive less attention. In this paper, we analyze the clauses of GDPR about privacy processing and propose a method for PII leakage detection based on Association Mining. This method assists us to find many hidden privacy leakages in traffic data. Moreover, we design and implement an automated system to detect whether the traffic data sent by the APPs reveals users' PII. We have tested 509 APPs of different categories in the Google Play Store. The result shows that 76.23% of the APPs would collect and transmit PII insecurely and 34.06% of them would send PII to third parties.
引用
收藏
页码:137 / 148
页数:12
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