Mobile network operators (e.g., China Mobile, Verizon) are significant for providing communication services and collecting massive amounts of data. However, operators are increasingly concerned about customer data breaches involving third-party application providers (e.g., Tencent, Apple, Netflix). This concern is particularly aggravated when anonymous datasets shared with third-party providers or publicly released can be linked to user data compromised in breaches, leading to severe re-identification attacks and privacy threats. However, comprehensive methods for identifying such privacy risks on a large scale are lacking due to limited networking behavioral data. To address this, we aim to measure the re-identification privacy risk associated with sharing or releasing cellular traces amidst data breaches. Based on the analysis of key privacyimpacting features in traffic usage and base station association data, we propose a novel re-identification method, SURE, which learns similarities between cellular traces to classify if traces belong to the same user. Extensive experiments on a largescale dataset of 10,000 users over four months demonstrate SURE's superior performance, with AUC scores exceeding 0.9. Our findings reveal significant re-identification risks in data sharing/release, influenced by data scale and user attributes, corroborated by a public dataset.

Effective contexts for separating shadows from non-shadow objects can appear in different scales due to different object sizes. This paper introduces a new module, Effective-Context Augmentation (ECA), to utilize these contexts for robust shadow detection with deep structures. Taking regular deep features as global references, ECA enhances the discriminative features from the parallelly computed fine-scale features and, therefore, obtains robust features embedded with effective object contexts by boosting them. We further propose a novel encoder-decoder style of shadow detection method where ECA acts as the main building block of the encoder to extract strong feature representations and the guidance to the classification process of the decoder. Moreover, the networks are optimized with only one loss, which is easy to train and does not have the instability caused by extra losses superimposed on the intermediate features among existing popular studies. Experimental results show that the proposed method can effectively eliminate fake detections. Especially, our method outperforms state-of-the-arts methods and improves over $13.97%$ and $34.67%$ on the challenging SBU and UCF datasets respectively in balance error rate.

Lei Cao, Huijun Zhang, Ling Feng, Zihan Wei, Xin Wang, Ningyun Li, Xiaohao He. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Visual sentiment analysis aims to recognize emotions from visual contents. It is a very useful yet challenging task, especially when fine-grained emotions (such as love, joy, surprise, sadness, fear, anger, disgust, and anxiety) are analyzed. Existing methods based on convolutional neural networks learn sentiment representations based on global visual features, while ignoring the fact that both the local regions of the images and their relationships can have impact on sentiment representation learning. To address this limitation, in this paper, we propose a new MultiAttentive Pyramidal model (MAP) for visual sentiment analysis. The model performs pyramidal segmentation and pooling upon the visual feature blocks obtained from a fully convolutional network, aiming to extract local visual features from multiple local regions at different scales of the global image. It then implants a self-attention mechanism to mine the associations between local visual features, and achieves the final sentiment representation. Extensive experiments on six benchmark datasets show the proposed MAP model outperforms the state-of-the-art methods in visual sentiment analysis.