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Predicting the socio-economic level of an urban region is of great significance for governments and city managers when allocating resources and making decisions. However, the current approaches for estimating regional socio-economic levels heavily rely on census data, which demands significant effort in terms of time and money. With the ubiquitous usage of smart phones and the prevalence of mobile applications, massive amounts of data are generated by mobile networks that record people’s behaviors. In this paper, we propose a low-cost approach of using humans’ online and offline indicators to predict the socio-economic levels of urban regions. The results show that the socio-economic prediction model that is trained using online and offline features extracted from these data achieves a high accuracy over 85%. Notably, online features are showed to be tightly linked with socio-economic development. In environments where censuses are rarely held, our method provides an option for timely and accurate prediction of the economic status of urban regions. [ABSTRACT FROM AUTHOR]

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack). [ABSTRACT FROM AUTHOR]