| 1. |
- Li, Fang, et al.
(författare)
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Lie Detection Using fNIRS Monitoring of Inhibition-Related Brain Regions Discriminates Infrequent but not Frequent Liars
- 2018
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Ingår i: Frontiers in Human Neuroscience. - : Frontiers Media S.A.. - 1662-5161. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Functional near-infrared spectroscopy (fNIRS) was used to test whether monitoring inhibition-related brain regions is a feasible method for detecting both infrequent liars and frequent liars. Thirty-two participants were divided into two groups: the deceptive group (liars) and the non-deceptive group (ND group, innocents). All the participants were required to undergo a simulated interrogation by a computer. The participants from the deceptive group were instructed to tell a mix of lies and truths and those of the ND group were instructed always to tell the truth. Based on the number of deceptions, the participants of the deceptive group were further divided into a infrequently deceptive group (IFD group, infrequent liars) and a frequently deceptive group (FD group, frequent liars). The infrequent liars exhibited greater neural activities than the frequent liars and the innocents in the left middle frontal gyrus (MFG) when performing the deception detection tasks. While performing deception detection tasks, infrequent liars showed significantly greater neural activation in the left MFG than the baseline, but frequent liars and innocents did not exhibit this pattern of neural activation in any area of inhibition-related brain regions. The results of individual analysis showed an acceptable accuracy of detecting infrequent liars, but an unacceptable accuracy of detecting frequent liars. These results suggest that using fNIRS monitoring of inhibition-related brain regions is feasible for detecting infrequent liars, for whom deception may be more effortful and therefore more physiologically marked, but not frequent liars.
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| 2. |
- Aronov, Boris, et al.
(författare)
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Are Friends of My Friends Too Social? Limitations of Location Privacy in a Socially-Connected World
- 2018
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Ingår i: PROCEEDINGS OF THE 2018 THE NINETEENTH INTERNATIONAL SYMPOSIUM ON MOBILE AD HOC NETWORKING AND COMPUTING (MOBIHOC 18). - New York, NY, USA : ASSOC COMPUTING MACHINERY. - 9781450357708 ; , s. 280-289
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Konferensbidrag (refereegranskat)abstract
- With the ubiquitous adoption of smartphones and mobile devices, it is now common practice for ones location to be sensed, collected and likely shared through social platforms. While such data can be helpful for many applications, users start to be aware of the privacy issue in handling location and trajectory data. While some users may voluntarily share their location information (e.g., for receiving location-based services, or for crowdsourcing systems), their location information may lead to information leaks about the whereabouts of other users, through the co-location of events when two users are at the same location at the same time and other side information, such as upper bounds of movement speed. It is therefore crucial to understand how much information one can derive about others positions through the co-location of events and occasional GPS location leaks of some of the users. In this paper we formulate the problem of inferring locations of mobile agents, present theoretically-proven bounds on the amount of information that could be leaked in this manner, study their geometric nature, and present algorithms matching these bounds. We will show that even if a very weak set of assumptions is made on trajectories patterns, and users are not obliged to follow any reasonable patterns, one could infer very accurate estimation of users locations even if they opt not to share them. Furthermore, this information could be obtained using almost linear-time algorithms, suggesting the practicality of the method even for huge volumes of data.
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