2,860 research outputs found
Social network dynamics of face-to-face interactions
The recent availability of data describing social networks is changing our
understanding of the "microscopic structure" of a social tie. A social tie
indeed is an aggregated outcome of many social interactions such as
face-to-face conversations or phone-calls. Analysis of data on face-to-face
interactions shows that such events, as many other human activities, are
bursty, with very heterogeneous durations. In this paper we present a model for
social interactions at short time scales, aimed at describing contexts such as
conference venues in which individuals interact in small groups. We present a
detailed anayltical and numerical study of the model's dynamical properties,
and show that it reproduces important features of empirical data. The model
allows for many generalizations toward an increasingly realistic description of
social interactions. In particular in this paper we investigate the case where
the agents have intrinsic heterogeneities in their social behavior, or where
dynamic variations of the local number of individuals are included. Finally we
propose this model as a very flexible framework to investigate how dynamical
processes unfold in social networks.Comment: 20 pages, 25 figure
Stigmatizedâ Identity Cues: Threats as Opportunities for Consumer Psychology
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146893/1/jcpy1076_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146893/2/jcpy1076.pd
Conclave: secure multi-party computation on big data (extended TR)
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to
run joint computations without revealing private data. Current MPC algorithms
scale poorly with data size, which makes MPC on "big data" prohibitively slow
and inhibits its practical use.
Many relational analytics queries can maintain MPC's end-to-end security
guarantee without using cryptographic MPC techniques for all operations.
Conclave is a query compiler that accelerates such queries by transforming them
into a combination of data-parallel, local cleartext processing and small MPC
steps. When parties trust others with specific subsets of the data, Conclave
applies new hybrid MPC-cleartext protocols to run additional steps outside of
MPC and improve scalability further.
Our Conclave prototype generates code for cleartext processing in Python and
Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave
scales to data sets between three and six orders of magnitude larger than
state-of-the-art MPC frameworks support on their own. Thanks to its hybrid
protocols, Conclave also substantially outperforms SMCQL, the most similar
existing system.Comment: Extended technical report for EuroSys 2019 pape
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