Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590453
Chen Liu, Pengyu Zhao, Kaigui Bian, Tong Zhao, Yan Wei
iBeacon is a new technology proposed by Apple using Bluetooth Low Energy (BLE), which helps mobile devices understand their position on a micro-local scale, and facilitates the delivery of location-based content to mobile users. According to the iBeacon protocol, iBeacon transmitters only do one-way broadcast communication, and thus it is difficult to authenticate the legitimacy of the transmitters. In this paper, we focus on detecting the physical attacks against iBeacon transmitters- iBeacon transmitters can be stolen, tampered, faked and reused, which interfere with the legitimate iBeacon transmission, reduce the quality of service and ultimately degrade the user experience. We develop a detection system based on Hidden Markov Model running on the server-side to detect the misbehaving iBeacon transmitters without more energy consuming. Our system achieves 85% detection accuracy on average for all physical attack actions; meanwhile it has a 5% false alarm rate for non-attack situations in our designated trace experiments, and 100% correct binary judgements in the random-trace experiments. To the best of our knowledge, this is the first work that focuses on the iBeacon physical attack problem.
{"title":"The detection of physical attacks against iBeacon transmitters","authors":"Chen Liu, Pengyu Zhao, Kaigui Bian, Tong Zhao, Yan Wei","doi":"10.1109/IWQoS.2016.7590453","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590453","url":null,"abstract":"iBeacon is a new technology proposed by Apple using Bluetooth Low Energy (BLE), which helps mobile devices understand their position on a micro-local scale, and facilitates the delivery of location-based content to mobile users. According to the iBeacon protocol, iBeacon transmitters only do one-way broadcast communication, and thus it is difficult to authenticate the legitimacy of the transmitters. In this paper, we focus on detecting the physical attacks against iBeacon transmitters- iBeacon transmitters can be stolen, tampered, faked and reused, which interfere with the legitimate iBeacon transmission, reduce the quality of service and ultimately degrade the user experience. We develop a detection system based on Hidden Markov Model running on the server-side to detect the misbehaving iBeacon transmitters without more energy consuming. Our system achieves 85% detection accuracy on average for all physical attack actions; meanwhile it has a 5% false alarm rate for non-attack situations in our designated trace experiments, and 100% correct binary judgements in the random-trace experiments. To the best of our knowledge, this is the first work that focuses on the iBeacon physical attack problem.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130806705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590430
Menghan Li, Dan Pei, Xiaoping Zhang, Beichuan Zhang, Zhi Wang, Hailiang Xu, Zihan Wang
IEEE 802.11-based wireless LAN, commonly referred to as Wi-Fi, has become a universal solution for the last-hop network access. In large and public assembly places, people may use their mobile devices to view the video of the same popular events via the same wireless access points (APs). However, current 802.11 APs transmit the same video stream multiple times via separate unicast sessions due to the well-known poor reliability and low data rate of the legacy Wi-Fi multicast. Besides, in traditional single-layer-coded video streams, all clients have to settle with the lowest video bitrate limited by the client with the worst channel quality. To address these problems, we propose M3, a practical and reliable multi-layer video multicast solution over multi-rate Wi-Fi networks. The aims of our system are, in the premise of no change to APs, not only to ensure that all clients can smoothly watch the video at least with the lowest quality, but also to maximize the overall video quality received by all clients. To meet these design goals, the video server selects certain clients as unicast receivers to transmit different SVC video layers, and other clients listen for the packets in the promiscuous mode. It is challenging to select specific unicast receivers and allocate different SVC layers to fully utilize the available bandwidth because of dynamic network conditions. To overcome this challenge, we use a periodical feedback mechanism to collect necessary statistics from clients, and use them to derive an optimal SVC-layer allocation strategy to maximize the video quality. We implemented a prototype in a real Wi-Fi testbed consisting of one AP and one M3 server and 8 clients. Compared with the single-layer video multicast, our M3 system can improve the total received video rate by up to 200%
{"title":"M3: Practical and reliable multi-layer video multicast over multi-rate Wi-Fi network","authors":"Menghan Li, Dan Pei, Xiaoping Zhang, Beichuan Zhang, Zhi Wang, Hailiang Xu, Zihan Wang","doi":"10.1109/IWQoS.2016.7590430","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590430","url":null,"abstract":"IEEE 802.11-based wireless LAN, commonly referred to as Wi-Fi, has become a universal solution for the last-hop network access. In large and public assembly places, people may use their mobile devices to view the video of the same popular events via the same wireless access points (APs). However, current 802.11 APs transmit the same video stream multiple times via separate unicast sessions due to the well-known poor reliability and low data rate of the legacy Wi-Fi multicast. Besides, in traditional single-layer-coded video streams, all clients have to settle with the lowest video bitrate limited by the client with the worst channel quality. To address these problems, we propose M3, a practical and reliable multi-layer video multicast solution over multi-rate Wi-Fi networks. The aims of our system are, in the premise of no change to APs, not only to ensure that all clients can smoothly watch the video at least with the lowest quality, but also to maximize the overall video quality received by all clients. To meet these design goals, the video server selects certain clients as unicast receivers to transmit different SVC video layers, and other clients listen for the packets in the promiscuous mode. It is challenging to select specific unicast receivers and allocate different SVC layers to fully utilize the available bandwidth because of dynamic network conditions. To overcome this challenge, we use a periodical feedback mechanism to collect necessary statistics from clients, and use them to derive an optimal SVC-layer allocation strategy to maximize the video quality. We implemented a prototype in a real Wi-Fi testbed consisting of one AP and one M3 server and 8 clients. Compared with the single-layer video multicast, our M3 system can improve the total received video rate by up to 200%","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126665282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590442
K. Ravindran, K. Fayzullaev, Yassine Wardei
Given cloud-based realization of a CDN service S (i.e., Content Distribution Network), QoS auditing captures the QoS violations that arise under various resource depletion and outage scenarios faced by S. Third-party control of the underlying cloud VM and storage nodes hosting the content (i.e., proxies of content server) raises the issue of reasoning about how well the CDN internal mechanisms are engineered to offer a required level of service to the application (i.e., low latency and overhead). We employ computational models of S to determine the optimal feasible proxy placements in the CDN topology and verify how close is the actual behavior of S to this 'gold standard'. Using declarative specifications, the QoS meta-data and CDN adaptation processes of S are externalized to enable a management module H reason about QoS violations. The paper elucidates the software and system engineering issues that arise in an external evaluation of the QoS provisioning of S by H.
{"title":"Model-based techniques for QoS assessment of cloud-hosted CDN services","authors":"K. Ravindran, K. Fayzullaev, Yassine Wardei","doi":"10.1109/IWQoS.2016.7590442","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590442","url":null,"abstract":"Given cloud-based realization of a CDN service S (i.e., Content Distribution Network), QoS auditing captures the QoS violations that arise under various resource depletion and outage scenarios faced by S. Third-party control of the underlying cloud VM and storage nodes hosting the content (i.e., proxies of content server) raises the issue of reasoning about how well the CDN internal mechanisms are engineered to offer a required level of service to the application (i.e., low latency and overhead). We employ computational models of S to determine the optimal feasible proxy placements in the CDN topology and verify how close is the actual behavior of S to this 'gold standard'. Using declarative specifications, the QoS meta-data and CDN adaptation processes of S are externalized to enable a management module H reason about QoS violations. The paper elucidates the software and system engineering issues that arise in an external evaluation of the QoS provisioning of S by H.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129191955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590446
Shanshan Wang, Zhenxiang Chen, Lei Zhang, Qiben Yan, Bo Yang, Lizhi Peng, Zhongtian Jia
Android has become the most popular mobile platform due to its openness and flexibility. Meanwhile, it has also become the main target of massive mobile malware. This phenomenon drives a pressing need for malware detection. In this paper, we propose TrafficAV, which is an effective and explainable detection of mobile malware behavior using network traffic. Network traffic generated by mobile app is mirrored from the wireless access point to the server for data analysis. All data analysis and malware detection are performed on the server side, which consumes minimum resources on mobile devices without affecting the user experience. Due to the difficulty in identifying disparate malicious behaviors of malware from the network traffic, TrafficAV performs a multi-level network traffic analysis, gathering as many features of network traffic as necessary. The proposed method combines network traffic analysis with machine learning algorithm (C4.5 decision tree) that is capable of identifying Android malware with high accuracy. In an evaluation with 8,312 benign apps and 5,560 malware samples, TCP flow detection model and HTTP detection model all perform well and achieve detection rates of 98.16% and 99.65%, respectively. In addition, for the benefit of user, TrafficAV not only displays the final detection results, but also analyzes the behind-the-curtain reason of malicious results. This allows users to further investigate each feature's contribution in the final result, and to grasp the insights behind the final decision.
{"title":"TrafficAV: An effective and explainable detection of mobile malware behavior using network traffic","authors":"Shanshan Wang, Zhenxiang Chen, Lei Zhang, Qiben Yan, Bo Yang, Lizhi Peng, Zhongtian Jia","doi":"10.1109/IWQoS.2016.7590446","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590446","url":null,"abstract":"Android has become the most popular mobile platform due to its openness and flexibility. Meanwhile, it has also become the main target of massive mobile malware. This phenomenon drives a pressing need for malware detection. In this paper, we propose TrafficAV, which is an effective and explainable detection of mobile malware behavior using network traffic. Network traffic generated by mobile app is mirrored from the wireless access point to the server for data analysis. All data analysis and malware detection are performed on the server side, which consumes minimum resources on mobile devices without affecting the user experience. Due to the difficulty in identifying disparate malicious behaviors of malware from the network traffic, TrafficAV performs a multi-level network traffic analysis, gathering as many features of network traffic as necessary. The proposed method combines network traffic analysis with machine learning algorithm (C4.5 decision tree) that is capable of identifying Android malware with high accuracy. In an evaluation with 8,312 benign apps and 5,560 malware samples, TCP flow detection model and HTTP detection model all perform well and achieve detection rates of 98.16% and 99.65%, respectively. In addition, for the benefit of user, TrafficAV not only displays the final detection results, but also analyzes the behind-the-curtain reason of malicious results. This allows users to further investigate each feature's contribution in the final result, and to grasp the insights behind the final decision.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133564371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590424
Dongliang Xie, Xin Wang, Linhui Ma
There is a big potential to enable more efficient data dissemination in mobile Delay-Tolerant Networks (DTNs) with the concurrent use of multi-copy forwarding and social metrics. However, this also leads to the possibility of severely overloading the relay nodes with high social metrics, and consequent performance degradation. We propose a fair source quota allocation algorithm to effectively alleviate the load while ensuring their dissemination fairness, i.e, Lexicographical order Max-Min Fairness(LMMF). In this paper, A fair source quota allocation algorithm along with an implementation scheme was presented to take advantage of the features of social networks and social forwarding for higher delivery performance. Extensive simulations based on trace data demonstrate that our mechanism greatly reduces the delivery-ratio degradation caused by uneven load while ensuring fairness among the network users.
{"title":"Lexicographical order Max-Min fair source quota allocation in mobile Delay-Tolerant Networks","authors":"Dongliang Xie, Xin Wang, Linhui Ma","doi":"10.1109/IWQoS.2016.7590424","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590424","url":null,"abstract":"There is a big potential to enable more efficient data dissemination in mobile Delay-Tolerant Networks (DTNs) with the concurrent use of multi-copy forwarding and social metrics. However, this also leads to the possibility of severely overloading the relay nodes with high social metrics, and consequent performance degradation. We propose a fair source quota allocation algorithm to effectively alleviate the load while ensuring their dissemination fairness, i.e, Lexicographical order Max-Min Fairness(LMMF). In this paper, A fair source quota allocation algorithm along with an implementation scheme was presented to take advantage of the features of social networks and social forwarding for higher delivery performance. Extensive simulations based on trace data demonstrate that our mechanism greatly reduces the delivery-ratio degradation caused by uneven load while ensuring fairness among the network users.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125749042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In data centers, a lot of cluster computing applications follow a coflow pattern. On the other hand, network function virtualization (NFV) sufficiently improves the performance of the data center network. However, coflows encounter extremely different processing delays under diverse network functions. Traditional coflow scheduling schemes become insufficient in this situation. Based on the observation that the benefit of coflows is closely related to the data rates of flows, we propose DRGC (Data Rate Guarantee for Coflow) to guarantee the data rate requirements of coflows in the NFV environment. We prioritize the scheduling sequence of coflows, precisely allocate data rates for individual flows, and design an efficient scheduling algorithm. DRGC maintains the desired data rates of coflows with higher priorities at middleboxes and leaves more scheduling opportunities to the ones with lower priorities. In the large-scale trace-driven experiment, DRGC efficiently guarantees the data rate requirements of coflows and supports more 15% workload, compared with other scheduling schemes.
在数据中心中，许多集群计算应用程序遵循coflow模式。另一方面，网络功能虚拟化(network function virtualization, NFV)充分提高了数据中心网络的性能。然而，在不同的网络功能下，共流的处理延迟差异很大。在这种情况下，传统的协同流调度方案显得有些不足。基于Coflow的效益与流的数据速率密切相关的观察，我们提出了DRGC (data Rate Guarantee for Coflow)来保证NFV环境下Coflow的数据速率要求。我们对协同流的调度顺序进行了优先级排序，精确地分配了各个流的数据速率，并设计了一种高效的调度算法。DRGC在中间盒中维护具有较高优先级的协同流所需的数据速率，并将更多的调度机会留给具有较低优先级的协同流。在大规模跟踪驱动实验中，与其他调度方案相比，DRGC有效地保证了协同流的数据速率要求，并支持15%以上的工作量。
{"title":"Data Rate Guarantee for Coflow scheduling in network function virtualization","authors":"Jianhui Zhang, Keqiu Li, Deke Guo, Heng Qi, Xiaoyi Tao, Yingwei Jin","doi":"10.1109/IWQoS.2016.7590440","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590440","url":null,"abstract":"In data centers, a lot of cluster computing applications follow a coflow pattern. On the other hand, network function virtualization (NFV) sufficiently improves the performance of the data center network. However, coflows encounter extremely different processing delays under diverse network functions. Traditional coflow scheduling schemes become insufficient in this situation. Based on the observation that the benefit of coflows is closely related to the data rates of flows, we propose DRGC (Data Rate Guarantee for Coflow) to guarantee the data rate requirements of coflows in the NFV environment. We prioritize the scheduling sequence of coflows, precisely allocate data rates for individual flows, and design an efficient scheduling algorithm. DRGC maintains the desired data rates of coflows with higher priorities at middleboxes and leaves more scheduling opportunities to the ones with lower priorities. In the large-scale trace-driven experiment, DRGC efficiently guarantees the data rate requirements of coflows and supports more 15% workload, compared with other scheduling schemes.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590428
Xinhou Wang, Kezhi Wang, Song Wu, S. Di, Kun Yang, Hai Jin
Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm.
{"title":"Dynamic resource scheduling in cloud radio access network with mobile cloud computing","authors":"Xinhou Wang, Kezhi Wang, Song Wu, S. Di, Kun Yang, Hai Jin","doi":"10.1109/IWQoS.2016.7590428","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590428","url":null,"abstract":"Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile users' devices to provide better quality of service (QoS). But the power consumption has become skyrocketing for MSP as it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MCC separately while less work had considered the integration of C-RAN with MCC. In this paper, we present a unifying framework for optimizing the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MCC to minimize the power consumption of MSP while still guaranteeing the QoS for mobile users. Our objective is to maximize the profit of MSP. To achieve this objective, we first formulate the resource scheduling issue as a stochastic problem and then propose a Resource onlIne sCHeduling (RICH) algorithm using Lyapunov optimization technique to approach a time average profit that is close to the optimum with a diminishing gap (1/V) for MSP while still maintaining strong system stability and low congestion to guarantee the QoS for mobile users. With extensive simulations, we demonstrate that the profit of RICH algorithm is 3.3× (18.4×) higher than that of active (random) algorithm.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134409776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590444
Kaixin Sui, Youjian Zhao, Dapeng Liu, Minghua Ma, Lei Xu, Zimu Li, Dan Pei
The enterprise Wi-Fi networks enable the collection of large-scale users' mobility information at an indoor level. The collected trajectory data is very valuable for both research and commercial purposes, but the use of the trajectory data also raises serious privacy concerns. A large body of work tries to achieve k-anonymity (hiding each user in an anonymity set no smaller than k) as the first step to solve the privacy problem. Yet it has been qualitatively recognized that k-anonymity is still risky when the diversity of the sensitive information in the k-anonymity set is low. There, however, still lacks a study that provides a quantitative understanding of that risk in the trajectory dataset. In this work, we present a large-scale measurement based analysis of the low-diversity risk over four weeks of trajectory data collected from Tsinghua, a campus that covers an area of 4 km2, on which 2,670 access points are deployed in 111 buildings. Using this dataset, we highlight the high risk of the low diversity. For example, we find that even when 5-anonymity is satisfied, the sensitive attributes of 25% of individuals can be easily guessed. We also find that although a larger k increases the size of anonymity sets, the corresponding improvement on the diversity of anonymity sets is very limited (decayed exponentially). These results suggest that diversity-oriented solutions are necessary.
{"title":"Your trajectory privacy can be breached even if you walk in groups","authors":"Kaixin Sui, Youjian Zhao, Dapeng Liu, Minghua Ma, Lei Xu, Zimu Li, Dan Pei","doi":"10.1109/IWQoS.2016.7590444","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590444","url":null,"abstract":"The enterprise Wi-Fi networks enable the collection of large-scale users' mobility information at an indoor level. The collected trajectory data is very valuable for both research and commercial purposes, but the use of the trajectory data also raises serious privacy concerns. A large body of work tries to achieve k-anonymity (hiding each user in an anonymity set no smaller than k) as the first step to solve the privacy problem. Yet it has been qualitatively recognized that k-anonymity is still risky when the diversity of the sensitive information in the k-anonymity set is low. There, however, still lacks a study that provides a quantitative understanding of that risk in the trajectory dataset. In this work, we present a large-scale measurement based analysis of the low-diversity risk over four weeks of trajectory data collected from Tsinghua, a campus that covers an area of 4 km2, on which 2,670 access points are deployed in 111 buildings. Using this dataset, we highlight the high risk of the low diversity. For example, we find that even when 5-anonymity is satisfied, the sensitive attributes of 25% of individuals can be easily guessed. We also find that although a larger k increases the size of anonymity sets, the corresponding improvement on the diversity of anonymity sets is very limited (decayed exponentially). These results suggest that diversity-oriented solutions are necessary.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133219138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590432
Huanyang Zheng, Wei Chang, Jie Wu
Traffic flow monitoring systems aim to measure and monitor vehicle trajectories in smart cities. Their critical applications include vehicle theft prevention, vehicle localization, and traffic congestion solution. This paper studies an RoadSide Unit (RSU) placement problem in traffic flow monitoring systems. Given some traffic flows on streets, the objective is to place a minimum number of RSUs to cover and distinguish all traffic flows. A traffic flow is covered and distinguishable, if the set of its passing RSUs is non-empty and unique among all traffic flows. The RSU placement problem is NP-hard, monotonic, and non-submodular. It is a non-trivial extension of the traditional set cover problem that is submodular. We show that, to cover and distinguish an arbitrary pair of traffic flows (f and f'), two RSUs should be placed on streets from two different subsets of ff', f'f, and f ∩ f'. Three bounded RSU placement algorithms are proposed. Their approximation ratios are n ln n(n-1)/2 , n+1/2 ln 3n(n-1)/2, and ln n(n+1)/2, respectively. Here, ri is the number of given traffic flows. Extensive real data-driven experiments demonstrate the efficiency and effectiveness of the proposed algorithms.
{"title":"Coverage and distinguishability requirements for Traffic Flow Monitoring Systems","authors":"Huanyang Zheng, Wei Chang, Jie Wu","doi":"10.1109/IWQoS.2016.7590432","DOIUrl":"https://doi.org/10.1109/IWQoS.2016.7590432","url":null,"abstract":"Traffic flow monitoring systems aim to measure and monitor vehicle trajectories in smart cities. Their critical applications include vehicle theft prevention, vehicle localization, and traffic congestion solution. This paper studies an RoadSide Unit (RSU) placement problem in traffic flow monitoring systems. Given some traffic flows on streets, the objective is to place a minimum number of RSUs to cover and distinguish all traffic flows. A traffic flow is covered and distinguishable, if the set of its passing RSUs is non-empty and unique among all traffic flows. The RSU placement problem is NP-hard, monotonic, and non-submodular. It is a non-trivial extension of the traditional set cover problem that is submodular. We show that, to cover and distinguish an arbitrary pair of traffic flows (f and f'), two RSUs should be placed on streets from two different subsets of ff', f'f, and f ∩ f'. Three bounded RSU placement algorithms are proposed. Their approximation ratios are n ln n(n-1)/2 , n+1/2 ln 3n(n-1)/2, and ln n(n+1)/2, respectively. Here, ri is the number of given traffic flows. Extensive real data-driven experiments demonstrate the efficiency and effectiveness of the proposed algorithms.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124281947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-20DOI: 10.1109/IWQoS.2016.7590392
Han Zhang, Xingang Shi, Xia Yin, Zhiliang Wang, Yingya Guo
Data Center is now becoming an important facility for many applications (e.g, web search and retail). As TCP can't meet applications' demands for latency and throughput, many tcp-based protocols (e.g, DCTCP, D2TCP, L2DCT) have been proposed. Among them, protocols such as D2TCP incorporate explicit deadline into congestion window adjustment procedure to guarantee flows' latency and protocols such as L2DCT consider flow size when computing congestion window adjustment factor to guarantee the throughput of short flows. These two methods work well at some scenery but they have some deficiencies on two aspects. Firstly, we find that they can only reduce the percentage of flows missing deadline or reduce flow completion time, but can not meet both the goals simultaneously. Secondly, most of these methods need the user to know flow information (e.g, deadline, flow size), which may be hard to know exact value beforehand. In this paper, we advocate to use flow duration time into congestion window adjustment procedure. Based on this, we propose FDRC-Flow Duration Time based Rate Control algorithm. We find that without knowing flow information beforehand, FDRC can achieve the goal of reducing the percentage of flows missing deadline and cutting average flow completion time simultaneously. We theoretically analyze FDRC's behavior and implement FDRC into ns-2 as well as linux kernel. Our experiments show that FDRC performs better than D2TCP and L2DCT at nearly all the scenarios. On average, it performs 30% better than the state-of-art deadline-aware congestion control protocol D2TCP and 10% better than the state-of-art flowsize-aware protocol L2DCT.
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