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A commonly used method to protect user privacy in data collection is to perform randomized perturbation on users real data before collection so that aggregated statistics can still be inferred without endangering secrets held by individuals. In this paper, we take a closer look at the validity of Differential Privacy guarantees, when the sensitive attributes are subject to social influence and contagions. We first show that in the absence of any knowledge about the contagion network, an adversary that tries to predict the real values from perturbed ones, cannot achieve an area under the ROC curve (AUC) above $1-(1-delta)/(1+e^varepsilon)$, if the dataset is perturbed using an $(varepsilon,delta)$-differentially private mechanism. Then, we show that with the knowledge of the contagion network and model, one can do significantly better. We demonstrate that our method passes the performance limit imposed by differential privacy. Our experiments also reveal that nodes with high influence on others are at more risk of revealing their secrets than others. The performance is shown through extensive experiments on synthetic and real-world networks.
The presence of correlation is known to make privacy protection more difficult. We investigate the privacy of socially contagious attributes on a network of individuals, where each individual possessing that attribute may influence a number of others
Recent research has focused on the monitoring of global-scale online data for improved detection of epidemics, mood patterns, movements in the stock market, political revolutions, box-office revenues, consumer behaviour and many other important pheno
In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educationa
One of the most significant challenges facing systems of collective intelligence is how to encourage participation on the scale required to produce high quality data. This paper details ongoing work with Phrase Detectives, an online game-with-a-purpo
The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser. Sharing such d