896 research outputs found

    ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ์ •์ฑ…๊ณผ ๋ชจ๋ฐ”์ผ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์ผ๊ด€์„ฑ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ๊ถŒํƒœ๊ฒฝ.๋ชจ๋ฐ”์ผ ์‚ฌ์šฉ์ž๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ, ๋ชจ๋ฐ”์ผ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์‚ฌ์šฉํ•˜๋Š” ๋™์•ˆ ๋ฏผ๊ฐํ•œ ๊ฐœ์ธ์ •๋ณด๊ฐ€ ์œ ์ถœ๋˜๋Š” ํ”„๋ผ์ด๋ฒ„์‹œ ๋ฌธ์ œ๊ฐ€ ๋งŽ์•„์กŒ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ตฌ๊ธ€ ์•ฑ์Šคํ† ์–ด์—์„œ๋Š” ๊ฐœ๋ฐœ์ž๋“ค์ด ์•ฑ์ด ๋ฐ์ดํ„ฐ๋ฅผ ์–ด๋–ป๊ฒŒ ํ™œ์šฉํ•˜๋Š”์ง€ ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ์ •์ฑ…์— ๊ณต๊ฐœํ•˜๋„๋ก ํ–ˆ๋‹ค. ์•ฑ ์ œ๊ณต์ž๋“ค์€ ๋ฒ•์ ์ธ ์š”๊ตฌ์‚ฌํ•ญ์„ ๋งŒ์กฑํ•˜๊ธฐ ์œ„ํ•ด ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ์ •์ฑ…์— ์•ฑ์˜ ํ™œ๋™์„ ๋ช…์‹œํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ์ •์ฑ…๊ณผ ์•ฑ ํ™œ๋™์˜ ์ผ๊ด€์„ฑ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ์ ์ธ ํ•ด๊ฒฐ์ฑ…์ด ์—†์œผ๋ฉฐ, ์‚ฌ์šฉ์ž๋Š” ์•ฑ์ด ๋ฐ์ดํ„ฐ๋ฅผ ์–ด๋–ป๊ฒŒ ํ™œ์šฉํ•˜๋Š”์ง€ ์•Œ๊ธฐ ์œ„ํ•ด ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ์ •์ฑ…์— ์˜์กดํ•ด์•ผ๋งŒ ํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์•ฑ์„ ํ†ตํ•ด ์‰ฝ๊ฒŒ ์œ ์ถœ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐœ์ธ์ •๋ณด ๋ชฉ๋ก์„ ์„ ์ •ํ•˜๊ณ  ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ์ •์ฑ…๊ณผ ์•ฑ์„ ๋ถ„์„ํ•ด ๋ฏผ๊ฐํ•œ ์ •๋ณด์™€ ๊ด€๋ จ์žˆ๋Š” ํ‚ค์›Œ๋“œ ๋ฐ API๋ฅผ ์ฐพ์•„ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•œ๋‹ค. ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ๊ตฌ๊ธ€ ํ”Œ๋ ˆ์ด ์Šคํ† ์–ด์— ๋“ฑ๋ก๋œ 13,223๊ฐœ ์•ฑ์˜ ํŒจํ‚ค์ง€ ํŒŒ์ผ๊ณผ ๋ถ€๊ฐ€์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ–ˆ๊ณ , ์ด๋ฅผ ์ „์ฒ˜๋ฆฌํ•˜์—ฌ ์‹คํ—˜ ๋Œ€์ƒ์ด ๋˜๋Š” ์•ฑ์„ ์„ ์ •ํ–ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ชจ๋ฐ”์ผ ์•ฑ์ด ํ”„๋ผ์ด๋ฒ„์‹œ ๋ณดํ˜ธ ์ •์ฑ…์— ๋ช…์‹œ๋œ ๊ฒƒ๋ณด๋‹ค ๋” ๋งŽ์€ ๊ฐœ์ธ์ •๋ณด์— ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ์Œ์„ ์ž…์ฆํ•œ๋‹ค.With the increase of mobile users, there have been many privacy issues that sensitive and personal data is leaked while using mobile applications. To deal with this problem, Google App Store has required developers to disclose how the app uses data in privacy protection policies. App developers describe all of the app practices in the privacy policy to meet these legal requirements. However, there is no technical solution to verify the consistency between privacy policies and app activities. Users must rely on privacy protection policies to see how the app uses data. In this paper, we select personal data categories that can be easily exposed through the app and analyze privacy policies and apps to find keywords and APIs related to personal data. We collected the APK files and metadata of 13,223 apps registered in Google Play Store as datasets and select the apps for analysis by preprocessing them by four conditions. According to the results, many apps can access more personal data than they disclosed in the privacy policy.Abstract i Chapter 1 Introduction 1 Chapter 2 Related work 4 2.1 Privacy Policy Analysis 4 2.2 Mobile App Analysis 5 Chapter 3 System Design 6 3.1 Overview 6 3.2 Playstore Crawler 7 3.3 Policy Analyzer 10 3.4 App Analyzer 11 Chapter 4 Result 13 4.1 Dataset 13 4.2 Personal Data Category 14 4.3 Consistency Check 17 4.4 App Genre 20 Chapter 5 Conclusion 23 Bibliography 24 ์ดˆ๋ก 29Maste

    A Security & Privacy Analysis of US-based Contact Tracing Apps

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    With the onset of COVID-19, governments worldwide planned to develop and deploy contact tracing (CT) apps to help speed up the contact tracing process. However, experts raised concerns about the long-term privacy and security implications of using these apps. Consequently, several proposals were made to design privacy-preserving CT apps. To this end, Google and Apple developed the Google/Apple Exposure Notification (GAEN) framework to help public health authorities develop privacy-preserving CT apps. In the United States, 26 states used the GAEN framework to develop their CT apps. In this paper, we empirically evaluate the US-based GAEN apps to determine 1) the privileges they have, 2) if the apps comply with their defined privacy policies, and 3) if they contain known vulnerabilities that can be exploited to compromise privacy. The results show that all apps violate their stated privacy policy and contain several known vulnerabilities

    Advanced Security Analysis for Emergent Software Platforms

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    Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya
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