38,484 research outputs found
Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles
Discriminatory caste attitudes currently stigmatize millions of Indians,
subjecting individuals to prejudice in all aspects of life. Governmental
incentives and societal movements have attempted to counter these attitudes,
yet accurate measurements of public opinions on caste are not yet available for
understanding whether progress is being made. Here, we introduce a novel
approach to measure public attitudes of caste through an indicator variable:
openness to intercaste marriage. Using a massive dataset of over 313K profiles
from a major Indian matrimonial site, we precisely quantify public attitudes,
along with differences between generations and between Indian residents and
diaspora. We show that younger generations are more open to intercaste
marriage, yet attitudes are based on a complex function of social status beyond
their own caste. In examining the desired qualities in a spouse, we find that
individuals open to intercaste marriage are more individualistic in the
qualities they desire, rather than favoring family-related qualities, which
mirrors larger societal trends away from collectivism. Finally, we show that
attitudes in diaspora are significantly less open, suggesting a bi-cultural
model of integration. Our research provides the first empirical evidence
identifying how various intersections of identity shape attitudes toward
intercaste marriage in India and among the Indian diaspora in the US.Comment: 12 pages; Accepted to be published at ICWSM'1
ConXsense - Automated Context Classification for Context-Aware Access Control
We present ConXsense, the first framework for context-aware access control on
mobile devices based on context classification. Previous context-aware access
control systems often require users to laboriously specify detailed policies or
they rely on pre-defined policies not adequately reflecting the true
preferences of users. We present the design and implementation of a
context-aware framework that uses a probabilistic approach to overcome these
deficiencies. The framework utilizes context sensing and machine learning to
automatically classify contexts according to their security and privacy-related
properties. We apply the framework to two important smartphone-related use
cases: protection against device misuse using a dynamic device lock and
protection against sensory malware. We ground our analysis on a sociological
survey examining the perceptions and concerns of users related to contextual
smartphone security and analyze the effectiveness of our approach with
real-world context data. We also demonstrate the integration of our framework
with the FlaskDroid architecture for fine-grained access control enforcement on
the Android platform.Comment: Recipient of the Best Paper Awar
PinMe: Tracking a Smartphone User around the World
With the pervasive use of smartphones that sense, collect, and process
valuable information about the environment, ensuring location privacy has
become one of the most important concerns in the modern age. A few recent
research studies discuss the feasibility of processing data gathered by a
smartphone to locate the phone's owner, even when the user does not intend to
share his location information, e.g., when the Global Positioning System (GPS)
is off. Previous research efforts rely on at least one of the two following
fundamental requirements, which significantly limit the ability of the
adversary: (i) the attacker must accurately know either the user's initial
location or the set of routes through which the user travels and/or (ii) the
attacker must measure a set of features, e.g., the device's acceleration, for
potential routes in advance and construct a training dataset. In this paper, we
demonstrate that neither of the above-mentioned requirements is essential for
compromising the user's location privacy. We describe PinMe, a novel
user-location mechanism that exploits non-sensory/sensory data stored on the
smartphone, e.g., the environment's air pressure, along with publicly-available
auxiliary information, e.g., elevation maps, to estimate the user's location
when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE
Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
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