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