16 research outputs found
Social Networks through the Prism of Cognition
Human relations are driven by social events - people interact, exchange
information, share knowledge and emotions, or gather news from mass media.
These events leave traces in human memory. The initial strength of a trace
depends on cognitive factors such as emotions or attention span. Each trace
continuously weakens over time unless another related event activity
strengthens it. Here, we introduce a novel Cognition-driven Social Network
(CogSNet) model that accounts for cognitive aspects of social perception and
explicitly represents human memory dynamics. For validation, we apply our model
to NetSense data on social interactions among university students. The results
show that CogSNet significantly improves quality of modeling of human
interactions in social networks
Influence of Personal Preferences on Link Dynamics in Social Networks
We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.Comment: 12 page
Hawkes-modeled telecommunication patterns reveal relationship dynamics and personality traits
It is not news that our mobile phones contain a wealth of private information
about us, and that is why we try to keep them secure. But even the traces of
how we communicate can also tell quite a bit about us. In this work, we start
from the calling and texting history of 200 students enrolled in the Netsense
study, and we link it to the type of relationships that students have with
their peers, and even with their personality profiles. First, we show that a
Hawkes point process with a power-law decaying kernel can accurately model the
calling activity between peers. Second, we show that the fitted parameters of
the Hawkes model are predictive of the type of relationship and that the
generalization error of the Hawkes process can be leveraged to detect changes
in the relation types as they are happening. Last, we build descriptors for the
students in the study by jointly modeling the communication series initiated by
them. We find that Hawkes-modeled telecommunication patterns can predict the
students' Big5 psychometric traits almost as accurate as the user-filled
surveys pertaining to hobbies, activities, well-being, grades obtained, health
condition and the number of books they read. These results are significant, as
they indicate that information that usually resides outside the control of
individuals (such as call and text logs) reveal information about the
relationship they have, and even their personality traits
LiveLabs: Building An In-Situ Real-Time Mobile Experimentation Testbed
We present LiveLabs, a mobile experimentation testbed that is cur-rently deployed across our university campus with further deploy-ments at a large shopping mall, a commercial airport, and a resort island soon to follow. The key goal of LiveLabs is to allow in-situ real-time experimentation of mobile applications and services that require context-specific triggers with real participants on their actual smart phones. We describe how LiveLabs works, and then explain the novel R&D required to realise it. We end with a de-scription of the current LiveLabs status (> 700 active participants to date) as well as present some key lessons learned. 1
Evaluating the Efficacy of Implicit Authentication Under Realistic Operating Scenarios
Smartphones contain a wealth of personal and corporate data. Several surveys have reported that about half of the smartphone owners do not configure primary authentication mechanisms (such as PINs, passwords, and fingerprint- or facial-recognition systems) on their devices to protect data due to usability concerns. In addition, primary authentication mechanisms have been subject to operating system flaws, smudge attacks, and shoulder surfing attacks. These limitations have prompted researchers to develop implicit authentication (IA), which authenticates a user by using distinctive, measurable patterns of device use that are gathered from the device users without requiring deliberate actions. Researchers have claimed that IA has desirable security and usability properties and it seems a promising candidate to mitigate the security and usability issues of primary authentication mechanisms.
Our observation is that the existing evaluations of IA have a preoccupation with accuracy numbers and they have neglected the deployment, usability and security issues that are critical for its adoption. Furthermore, the existing evaluations have followed an ad-hoc approach based on synthetic datasets and weak adversarial models. To confirm our observations, we first identify a comprehensive set of evaluation criteria for IA schemes. We gather real-world datasets and evaluate diverse and prominent IA schemes to question the efficacy of existing IA schemes and to gain insight into the pitfalls of the contemporary evaluation approach to IA. Our evaluation confirms that under realistic operating conditions, several prominent IA schemes perform poorly across key evaluation metrics and thereby fail to provide adequate security.
We then examine the usability and security properties of IA by carefully evaluating promising IA schemes. Our usability evaluation shows that the users like the convenience offered by IA. However, it uncovers issues due to IA's transparent operation and false rejects, which are both inherent to IA. It also suggests that detection delay and false accepts are concerns to several users. In terms of security, our evaluation based on a realistic, stronger adversarial model shows the susceptibility of highly accurate, touch input-based IA schemes to shoulder surfing attacks and attacks that train an attacker by leveraging raw touch data of victims. These findings exemplify the significance of realistic adversarial models.
These critical security and usability challenges remained unidentified by the previous research efforts due to the passive involvement of human subjects (only as behavioural data sources). This emphasizes the need for rapid prototyping and deployment of IA for an active involvement of human subjects in IA research. To this end, we design, implement, evaluate and release in open source a framework, which reduces the re-engineering effort in IA research and enables deployment of IA on off-the-shelf Android devices.
The existing authentication schemes available on contemporary smartphones fail to provide both usability and security. Authenticating users based on their behaviour, as suggested by the literature on IA, is a promising idea. However, this thesis concludes that several results reported in the existing IA literature are misleading due to the unrealistic evaluation conditions and several critical challenges in the IA domain need yet to be resolved. This thesis identifies these challenges and provides necessary tools and design guidelines to establish the future viability of IA