You Can Yak But You Can't Hide

Nemelka, Carson L.; Ballard, Cameron; Liu, Kelvin; Xue, Minhui; Ross, Keith W.

doi:10.1145/2817946.2820605

Cited by 8 publications

(3 citation statements)

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“…In general, some attackers try to steal the identity and location information of users in the graph by exploiting the various attributes of the node. Xue et al utilize supervised machine learning to analyze the security of Yik Yak, a popular anonymous social network in college. In experiment, they even succeed in locating the dormitory where anonymous messages are sent.…”

Section: Techniques For Big Data Security and Privacymentioning

confidence: 99%

Challenges and techniques in Big data security and privacy: A review

Bao

Chen

Obaidat

2018

Security and Privacy

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With the rapid development of information technology, Big data has become a hot topic of research in governments, academia, and enterprises. On the one hand, Big data brings opportunities that are not available by small‐scale data in many fields, such as business, education, and healthcare. On the other hand, due to the huge amount, the development of Big data is still facing many security and privacy issues in the whole lifetime of Big data. Security and privacy issues not only plague users and businesses, but also create obstacles to the expected opportunities and progress of Big data. In this study, we first review challenges in Big data security and privacy, especially considering the challenges in different lifetime of Big data. Then we review four popular secure technologies: homomorphic encryption, secure multiparty computation, attribute‐based encryption, and anonymous protection in social network. Based on the reviews, we identify recommendations and 3 possible directions for security and privacy protection of Big data.

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Section: Techniques For Big Data Security and Privacymentioning

confidence: 99%

Challenges and techniques in Big data security and privacy: A review

Bao

Chen

Obaidat

2018

Security and Privacy

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“…8 In the same year, Facebook released its own IoT platform, Parse. The combination of mobile social networks and IoT makes mobile social networks can not only meet the demand of users for people-to-people interaction, [9][10][11] but also promote further development of IoT. As there are a great number of mobile social network users, malicious users among them may conduct rumor spreading, virus dissemination, and other malicious behaviors, [12][13][14] which seriously endanger the health of network environment and the security of IoT.…”

Section: Introductionmentioning

confidence: 99%

Geolocating a WeChat user based on the relation between reported and actual distance

Shi

Luo

Zhao

et al. 2018

International Journal of Distributed Sensor Networks

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The combination of social networks and the Internet of Things has raised a new wave of network technology application. However, the presence of malicious social network users poses a potential threat to Internet of Things security, and the research on social network user geolocation technology is of great significance. The accuracy of existing geolocation methods for WeChat users depends on the stable correspondence between the reported distance and actual distance. In view of the difficulty to pinpoint users' location in the real world due to WeChat location protection strategy, a WeChat user geolocation algorithm based on the reported and actual distance relation analysis is proposed. The proposed algorithm selects the target reported distance and determines the initial target space based on statistical characteristics of the relation between the reported distance and actual distance. What is more, stepwise strategies are taken to improve the accuracy rate of space partition. Experimental results show that, on the premise that target users can be discovered, the proposed algorithm could achieve higher accuracy compared with the classical space partition-based algorithm and the heuristic number theory based algorithm. The highest geolocation accuracy is within 10 m and 56% of geolocation results are within 60 m.

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“…As one of the fastest‐growing dating application in the United States, Tinder is soon approaching 50 million active users who log into the application 11 times a day on average. Yik Yak creates an anonymous social network experience and enjoys enormous popularity on US college and university campuses .…”

Section: Introductionmentioning

confidence: 99%

Thwarting location privacy protection in location‐based social discovery services

Xue

Liu

Ross

et al. 2016

Security Comm Networks

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Location‐based social discovery (LBSD) services enable users to discover their geographic neighborhoods to make new friends. Original LBSD services were designed to provide the exact distances to nearby users. It has been shown that it is easy to pinpoint any target user's location by using trilateration based on the exact distances from three fake Global Positioning System locations to the target user. To defend against the trilateration attack, contemporary LBSD services then began to report distances of nearby users in concentric bands, for example, bands of 100 meters, rather than exact distances. In this paper, we investigate the user location privacy leakage problem in LBSD services reporting distances in discrete bands. Using number theory, we analytically show that by strategically placing multiple virtual probes with fake Global Positioning System locations, one can nevertheless localize user locations in band‐based LBSD. Our methodology is guaranteed to localize any reported user within a circle of radius no greater than one meter, even for LBSD services using large bands (such as 100 m as used by WeChat). Eventually, countermeasures are proposed to reduce location privacy leakage to the very minimum. To the best of our knowledge, this is the first work that explicitly exploits and quantifies user location privacy leakage in band‐based LBSD services. We expect our study to draw more public attention to this serious privacy issue and expectantly motivate better privacy preserving LBSD designs. Copyright © 2016 John Wiley & Sons, Ltd.

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You Can Yak But You Can't Hide

Cited by 8 publications

References 0 publications

Challenges and techniques in Big data security and privacy: A review

Challenges and techniques in Big data security and privacy: A review

Geolocating a WeChat user based on the relation between reported and actual distance

Thwarting location privacy protection in location‐based social discovery services

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