,0.000.00.00 0.00 1.00 C, 0.00 0.00 0.12 0.88 0.00 0.02 0.00 0.88 0.10 0.00 ec, 0.24048 0.28 0.00 0.00 c.1.00.00.00 0.000.00 C C.C,C Inferred closeness cutegury (a)Demographics Inference Results (b)Demographics Inferenc ewit (a)Classification confusion matrix of (b)Classification of detailed different observation time. 4 kinds of physical closeness. daily routine-based places. Fig.12.Accuracy of behavior-based demographics inference Fig.13.Classification accuracy of physical closeness and daily routine-based C.Evaluation of Demographics Inference places. 1)Accuracy of Demographics Inference: Figure 12(a) information in large-scale areas),we evaluate our system by shows the overall accuracy of inferring demographics.For all recruiting 21 volunteers with representative occupations and the demographics in our study,our system achieves over 90.5% social relationship types.Furthermore,the study is based on accuracy for Occupation,Religion and Marriage,whereas the the users'daily life activities across three cities without being accuracy of gender inference is 95.2%for the 21 volunteers, restricted in a confined area.Since the participants'activities suggesting that it is possible to accurately infer people's at daily places are employed as the inference basis in this demographics from surrounding AP information.We further work,we believe our system has the capability to successfully study the performance of gender and occupation inference with infer fine-grained social relationships and demographics in different length of observation time as shown in Figure 12(b) larger areas when given the opportunity.We demonstrate The inference results converge after 5 days,suggesting that that the privacy leakage from the simple signal information people's behavior features derived in a short period(i.e.,one of surrounding APs is significant and should arouse public week)can accurately infer the demographics attention.For the future work,we will continue our efforts to 2)Fine-grained Social Relationships Derived from Demo- enlarge the Wi-Fi AP dataset and investigate more potential graphics:By leveraging the derived demographics informa-privacy leakages from such simple radio signals surrounding tion,we further obtained refined relationships.Based on our daily lives. the gender information,we successfully detect all the two IX.CONCLUSION couples from the 21 volunteers.Besides,from the occupation In this paper,we show that by analyzing the information inference,we specify the relationship of collaborators,e.g. from surrounding Wi-Fi Access Points(APs),the users'fine- who is superior and who is subordinate.In specifically,we grained social relationships and demographics could be dis- correctly differentiate 4 superior-subordinate from 5 collab- closed.We present a scalable inference system that has the orator pairs.These results show it is possible to accurately potential to derive people's activities at daily visited places infer fine-grained social relationships and demographics from leveraging surrounding APs and utilize such information to surrounding AP information. infer fine-grained social relationships and demographics.This D.Performance of Daily Place Extraction implemented system only uses the simple signal features of We randomly select 100 staying segments to examine surrounding APs such as MAC addresses and Received Signal whether our different levels of physical closeness can reflect Strength without requiring to obtain the context information the true relations between their physical locations.Figure 13(a) by sniffing the Wi-Fi traffic.In particular,we describe peo- presents the confusion matrix of the inferred four kinds of ple's daily places in three dimensions (i.e.time,space and closenesses and the results show that our system can achieve context)to infer people's activities and extract their activity over 88%accuracy for measuring most levels of closeness features as well as their physical closeness at same places except for Cl,whose inference relies on the remote APs or Our Closeness-based Social Relationships Inference algorithm unstable signals.We note that the lowest level Cl does not further analyzes people's physical closeness to capture when, affect the social relationships and demographics inference as where and how closely people interact to reveal fine-grained both of them mainly rely on C4 and C3. social relationships,while the Behavior-based Demographics Finally,we evaluate the accuracy of the contextual meaning Inference method extracts people's various individual behavior inference with 594 detected places.Figure 13(b)shows we can from their activity features to infer demographics.By using achieve over 90%accuracy for Workplace and Home and over the data collected by 21 participants in their daily lives 80%accuracy for detailed Leisure places (e.g.,Shop,Diner, over 6 months,our system confirms the possibility of using Church and Other).The results demonstrate the possibility surrounding APs to infer people's social relationships and to measure the physical closeness between places and infer demographics with over 90%accuracy. complex contextual meaning of daily places only from user's ACKNOWLEDGMENT surrounding APs. This work is supported in part by the NSF grants VIII.DISCUSSION CNS1514436,CNS1409767,the NSF of China grant Due to the limited manpower and shortage of public avail- 61472185 and the JiangSu Natural Science Foundation grant able data sources (i.e.,containing the scanned AP signal BK20151390.Demografic types Occupation Gender Marriage Religion Accuracy 0 0.2 0.4 0.6 0.8 1 Observation time (day) 12345678 Accuracy 0 0.2 0.4 0.6 0.8 1 Gender Occupation (a) Demographics Inference Results. (b) Demographics Inference with different observation time. Fig. 12. Accuracy of behavior-based demographics inference. C. Evaluation of Demographics Inference 1) Accuracy of Demographics Inference: Figure 12(a) shows the overall accuracy of inferring demographics. For all the demographics in our study, our system achieves over 90.5% accuracy for Occupation, Religion and Marriage, whereas the accuracy of gender inference is 95.2% for the 21 volunteers, suggesting that it is possible to accurately infer people’s demographics from surrounding AP information. We further study the performance of gender and occupation inference with different length of observation time as shown in Figure 12(b). The inference results converge after 5 days, suggesting that people’s behavior features derived in a short period (i.e., one week) can accurately infer the demographics. 2) Fine-grained Social Relationships Derived from Demo￾graphics: By leveraging the derived demographics informa￾tion, we further obtained refined relationships. Based on the gender information, we successfully detect all the two couples from the 21 volunteers. Besides, from the occupation inference, we specify the relationship of collaborators, e.g. who is superior and who is subordinate. In specifically, we correctly differentiate 4 superior-subordinate from 5 collab￾orator pairs. These results show it is possible to accurately infer fine-grained social relationships and demographics from surrounding AP information. D. Performance of Daily Place Extraction We randomly select 100 staying segments to examine whether our different levels of physical closeness can reflect the true relations between their physical locations. Figure 13(a) presents the confusion matrix of the inferred four kinds of closenesses and the results show that our system can achieve over 88% accuracy for measuring most levels of closeness except for C1, whose inference relies on the remote APs or unstable signals. We note that the lowest level C1 does not affect the social relationships and demographics inference as both of them mainly rely on C4 and C3. Finally, we evaluate the accuracy of the contextual meaning inference with 594 detected places. Figure 13(b) shows we can achieve over 90% accuracy for Workplace and Home and over 80% accuracy for detailed Leisure places (e.g., Shop, Diner, Church and Other). The results demonstrate the possibility to measure the physical closeness between places and infer complex contextual meaning of daily places only from user’s surrounding APs. VIII. DISCUSSION Due to the limited manpower and shortage of public avail￾able data sources (i.e., containing the scanned AP signal Inferred closeness category C0 C1 C2 C3 C4 Actual closeness category C0 C1 C2 C3 C4 1.00 0.24 0.02 0.00 0.00 0.00 0.48 0.00 0.00 0.00 0.00 0.28 0.88 0.12 0.00 0.00 0.00 0.10 0.88 0.00 0.00 0.00 0.00 0.00 1.00 Work Home Shop Diner Church Other Accuracy 0 0.2 0.4 0.6 0.8 1 (a) Classification confusion matrix of (b) Classification of detailed 4 kinds of physical closeness. daily routine-based places. Fig. 13. Classification accuracy of physical closeness and daily routine-based places. information in large-scale areas), we evaluate our system by recruiting 21 volunteers with representative occupations and social relationship types. Furthermore, the study is based on the users’ daily life activities across three cities without being restricted in a confined area. Since the participants’ activities at daily places are employed as the inference basis in this work, we believe our system has the capability to successfully infer fine-grained social relationships and demographics in larger areas when given the opportunity. We demonstrate that the privacy leakage from the simple signal information of surrounding APs is significant and should arouse public attention. For the future work, we will continue our efforts to enlarge the Wi-Fi AP dataset and investigate more potential privacy leakages from such simple radio signals surrounding our daily lives. IX. CONCLUSION In this paper, we show that by analyzing the information from surrounding Wi-Fi Access Points (APs), the users’ fine￾grained social relationships and demographics could be dis￾closed. We present a scalable inference system that has the potential to derive people’s activities at daily visited places leveraging surrounding APs and utilize such information to infer fine-grained social relationships and demographics. This implemented system only uses the simple signal features of surrounding APs such as MAC addresses and Received Signal Strength without requiring to obtain the context information by sniffing the Wi-Fi traffic. In particular, we describe peo￾ple’s daily places in three dimensions (i.e. time, space and context) to infer people’s activities and extract their activity features as well as their physical closeness at same places. Our Closeness-based Social Relationships Inference algorithm further analyzes people’s physical closeness to capture when, where and how closely people interact to reveal fine-grained social relationships, while the Behavior-based Demographics Inference method extracts people’s various individual behavior from their activity features to infer demographics. By using the data collected by 21 participants in their daily lives over 6 months, our system confirms the possibility of using surrounding APs to infer people’s social relationships and demographics with over 90% accuracy. ACKNOWLEDGMENT This work is supported in part by the NSF grants CNS1514436, CNS1409767, the NSF of China grant 61472185 and the JiangSu Natural Science Foundation grant BK20151390