58:, which can be accessed by third-party apps without user permission in many mobile devices, has been used to infer rich information about users based on the recorded motion patterns (e.g., driving behavior, level of intoxication, age, gender, touchscreen inputs, geographic location). Highly sensitive inferences can also be derived, for example, from
39:. An Inference attack occurs when a user is able to infer from trivial information more robust information about a database without directly accessing it. The object of Inference attacks is to piece together information at one security level to determine a fact that should be protected at a higher security level.
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Kröger, Jacob Leon; Lutz, Otto Hans-Martin; Raschke, Philip (2020). "Privacy
Implications of Voice and Speech Analysis – Information Disclosure by Inference".
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Kröger, Jacob Leon; Lutz, Otto Hans-Martin; Müller, Florian (2020). "What Does Your Gaze Reveal About You? On the
Privacy Implications of Eye Tracking".
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Kröger, Jacob Leon; Raschke, Philip (January 2019). "Privacy implications of accelerometer data: a review of possible inferences".
100:"Protecting Individual Information Against Inference Attacks in Data Publishing" by Chen Li, Houtan Shirani-Mehr, and Xiaochun Yang
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can be considered as leaked if an adversary can infer its real value with a high confidence. This is an example of breached
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Sankar, Lalitha; Rajagopalan, S.R.; Mohajer, Soheil; Poor, H.V. (2013). "Smart Meter
Privacy: A Theoretical Framework".
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Proceedings of the 2014 ACM International Joint
Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
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Clement, Jana; Ploennigs, Joern; Kabitzsch, Klaus (2014). "Detecting
Activities of Daily Living with Smart Meters".
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Liebling, Daniel J.; Preibusch, Sören (2014). "Privacy considerations for a pervasive eye tracking world".
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technique performed by analyzing data in order to illegitimately gain knowledge about a subject or
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110:"Detecting Inference Attacks Using Association Rules" by Sangeetha Raman, 2001
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Privacy and
Identity Management. Data for Better Living: AI and Privacy
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Privacy and
Identity Management. Data for Better Living: AI and Privacy
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While inference attacks were originally discovered as a threat in
327:. Advanced Technologies and Societal Change. pp. 143–160.
175:"Sensor Guardian: prevent privacy inference on Android sensors"
46:, today they also pose a major privacy threat in the domain of
152:. Macmillan International Higher Education. pp. 11–.
122:""Database Security Issues: Inference" by Mike Chapple"
87:"Inference Attacks on Location Tracks" by John Krumm
98:http://www.ics.uci.edu/~chenli/pub/2007-dasfaa.pdf
173:Bai, Xiaolong; Yin, Jie; Wang, Yu-Ping (2017).
149:Security of Computer Based Information Systems
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179:EURASIP Journal on Information Security
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218:. ACM, New York. pp. 81–87.
66:data and voice recordings (e.g.,
366:IEEE Transactions on Smart Grid
146:V. P. Lane (8 November 1985).
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31:. A subject's sensitive
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225:10.1145/3309074.3309076
54:sensor data. Data from
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44:statistical databases
16:Data mining technique
37:information security
457:Applied data mining
428:978-3-030-42503-6
342:978-3-642-37987-1
301:978-3-030-42503-6
159:978-1-349-18011-0
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60:eye tracking
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64:smart meter
33:information
25:data mining
451:Categories
132:2007-10-23
74:References
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386:1949-3053
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185:(1).
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183:2017
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