INFOCOM 2018 论文列表

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, FOG incurs a very small throughput hit of ≈5 % with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of uncertainty, including vehicles' travel times to their dispatched locations, as well as coordinating between vehicles so that they do not attempt to pick up the same passenger. While prior works have developed models for this uncertainty and used them to optimize dispatch policies, in this work we introduce a model-free approach. Specifically, we propose MOVI, a Deep Q-network (DQN)-based framework that directly learns the optimal vehicle dispatch policy. Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles. We then formulate a centralized receding-horizon control (RHC) policy to compare with our DQN policies. To compare these policies, we design and build MOVI as a large-scale realistic simulator based on 15 million taxi trip records that simulates policy-agnostic responses to dispatch decisions. We show that the DQN dispatch policy reduces the number of unserviced requests by 76% compared to without dispatch and 20% compared to the RHC approach, emphasizing the benefits of a model-free approach and suggesting that there is limited value to coordinating vehicle actions. This finding may help to explain the success of ridesharing platforms, for which drivers make individual decisions.

Dynamic Host Configuration Protocol (DHCP) is widely used to dynamically assign IP addresses. However, due to little knowledge on the behavior and performance of DHCP, it is challenging to configure a proper lease time in complicated wireless network. In this paper, we conduct the largest known measurement on the behavior and performance of DHCP based on the wireless network of T University (TWLAN). TWLAN has more than 59,000 users, 10,000 wireless access points and 130,000 IP addresses. We find the performance of DHCP is far from satisfactory: (1) The non-authenticated devices lead to a waste of 25% of IP addresses at the rush hour. (2) A device does not generate traffic for 67 % of the lease time on average. Meanwhile, we find devices of different locations and operating systems show diverse online patterns. A unified lease time setting could result in an inefficient utilization of addresses. To address the problems, taking account of authentication information and device online patterns, we propose a new leasing strategy. The results show it reduces the number of assigned addresses by 24 % and reduces the time during which an IP address is occupied by 17 % without sianificantly Increasing the DHCP server load.

TOR is a well-known and established anonymous network that has increasingly been abused by services distributing and hosting content, in most cases images and videos, that are illegal or morally deplorable (e.g., child pornography content). Law enforcement continually tries to identify the users and providers of such content. State of the art techniques to breach TOR's anonymity are usually based on passive and active network traffic analysis, and rely on the ability of the deanonymization entity to control TOR's edge communication. Despite this, locating hidden servers and linking illegal content with those providing and spreading this content remains an open and controversial issue. In this paper, we describe Inflow, a new technique to identify hidden servers based on inverse flow watermarking. Inflow exploits the influence of congestion mechanisms on the traffic passing through the TOR network. Inflow drops bursts of packets for short time intervals on the receiving side of a traffic flow coming from a hidden server and passing through the TOR network. Packet dropping affects the TOR flow control and causes time gaps in flows observed on the hidden server side. By controlling the communication edges and detecting the watermarking gaps, Inflow is able to detect the hidden server. Our results, obtained by means of empirical experiments performed on the real TOR network, show true positive rates in the range of 90 to 98%.

Power-of-d-choices is a popular load balancing algorithm for many-server systems such as large-scale data centers. For each incoming job, the algorithm probes d servers, chosen uniformly at random from a total of N servers, and routes the job to the least loaded one. It is well known that power-of-d-choices reduces queueing delays by orders of magnitude compared to the policy that routes each incoming job to a randomly selected server. The question to be addressed in this paper is how large d needs to be so that power-of-d-choices achieves asymptotic zero delay like the join-the-shortest-queue (JSQ) algorithm, which is a special case of power-of-d-choices with d=N. We are interested in the heavy-traffic regime where the load of the system, denoted by λ, approaches to one as N increases, and assume λ = 1-γN-αfor and . This paper establishes that when d=ω-([1/(1-λ)]), the probability that an incoming job is routed to a busy server is asymptotically zero, i.e. a job experiences zero queueing delay with probability one asymptotically; and when d=O([1/(1-λ)])' the probability that a job is routed to a busy server is lower bounded by a positive constant independent of N. Therefore, our results show that d=ω([1/(1-λ)]) is sufficient and almost necessary for achieving zero delay with the power-of-d-choices load balancing policy.

While automated network verification is emerging as a critical enabler to manage large complex networks, current approaches come with a high computational complexity. This paper initiates the study of communication networks whose configurations can be verified fast, namely in polynomial time. In particular, we show that in communication networks based on prefix rewriting, which include MPLS networks, important network properties such as reachability, loop-freedom, and transparency, can be verified efficiently, even in the presence of failures. This enables a fast what-if analysis, addressing a major concern of network administrators: while configuring and testing network policies for a fully functional network is challenging, ensuring policy compliance in the face of (possibly multiple) failures, is almost impossible for human administrators. At the heart of our approach lies an interesting connection to the theory of prefix rewriting systems, a subfield of language and automata theory.

Network virtualization enables increasingly diverse network services to cohabit and share a given physical infrastructure and its resources, with the possibility to rely on different network architectures and protocols optimized towards specific requirements. In order to ensure a predictable performance despite shared resources, network virtualization requires a strict performance isolation and hence, resource reservations. Moreover, the creation of virtual networks should be fast and efficient. The underlying NP-hard algorithmic problem is known as the Virtual Network Embedding (VNE) problem and has been studied intensively over the last years. This paper presents NeuroViNE, a novel approach to speed up and improve a wide range of existing VNE algorithms: NeuroViNE is based on a search space reduction mechanism and preprocesses a problem instance by extracting relevant subgraphs, i.e., good combinations of substrate nodes and links. These subgraphs can then be fed to an existing algorithm for faster and more resource-efficient embeddings. NeuroViNE relies on a Hopfield network, and its performance benefits are investigated in simulations for random networks, real substrate networks, and data center networks.

Searchable encryption enables searches to be performed on encrypted documents stored on an untrusted server without exposing the documents or the search terms to the server. Nevertheless, the server typically learns which encrypted documents match the query-the so-called access pattern-since the server must return those documents. Recent studies have demonstrated that access patterns can be used to infer the search terms in some scenarios. In this paper, we propose a framework to protect systems using searchable symmetric encryption from access-pattern leakage. Our technique is based on d-privacy, a generalized version of differential privacy that provides provable security guarantees against adversaries with arbitrary background knowledge.

One critical issue for wireless power transfer is to avoid human health impairments caused by electromagnetic radiation (EMR) exposure. The existing studies mainly focus on scheduling wireless chargers so that (expected) EMR at any point in the area doesn't exceed a threshold Rt. Nevertheless, they overlook the EMR jitter that leads to exceeding of Rteven if the expected EMR is no more than Rt. This paper studies the fundamental problem of RObustly SafE charging for wireless power transfer (ROSE), that is, scheduling the power of chargers so that the charging utility for all rechargeable devices is maximized while the probability that EMR anywhere doesn't exceed Rt is no less than a given confidence. We first build our empirical probabilistic charging model and EMR model. Then, we present EMR approximation and area discretization techniques to formulate ROSE into a Second-Order Cone Program, and the first redundant second-order cone constraints reduction algorithm to reduce the computational cost, and therefore obtain a (1-ε)-approximation centralized algorithm. Further, we propose a (1-ε)-approximation fully distributed algorithm scalable with network size for ROSE. Simulations and field experiments show that our algorithms can outperform comparison algorithms by 480.19%.

Cloud service brokerage (CSB), which procures cloud services from multiple cloud service providers (CSPs) and resells them to cloud customers, has been put forward to facilitate the delivery of cloud services. However, it is challenging to address the economic issues of CSB incurred by insufficient provisioning problem in response to dynamic conditions, for example, dynamic customer demands, dynamic cloud service prices and different availabilities of CSPs. In this paper, we propose a novel mechanism called CSB Demand Response (DR-CSB), which aims to maximize the profit of CSB under dynamic customer demands with respect to the capacity and availability constraints, to mitigate the insufficient provisioning problem. To this end, we formulate an optimization problem of profit maximization for the CSB, and employ economic demand response mechanism to allow cloud customers to adjust their consumptions with dynamic cloud service prices. Our evaluations driven by Google cluster-usage traces have verified that the DR-CSB not only can help the CSB to achieve the profit maximization, but also can handle the impact of the dynamic conditions in CSB. As the result shows, the profit of CSB with implementing DR-CSB can increase by up to 20%, and customers also achieve a 37% aggregated cost saving, compared with the scenario without DR-CSB.

In Cognitive Radio Networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utility. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. A reward of a channel is stochastic with unknown distribution, and statistically different for each user. Each user either explores a channel to learn the channel statistics, or exploits the channel with the highest expected reward based on information collected so far. Further, a channel should be accessed exclusively by one user at a time due to a collision. Using multi-armed bandit framework, we develop a provably efficient solution whose computational complexity is linear to the number of users and channels.

Future services and applications, such as Tactile Internet, coordinated remote driving or wireless controlled exoskeletons, pose serious challenges on the underlying networks and IT platforms in terms of reliability, latency, or capacity, just to mention a few. Towards those services, virtualization is a key enabler from both technological and economic aspects which significantly reshaped the IT and networking ecosystem. On the one hand, cloud computing and the services based on that are evident results of last years' efforts; on the other hand, networking is in the middle of a momentous revolution and important changes mainly driven by Network Function Virtualization (NFV) and Software Defined Networking (SDN). In order to enable carrier grade network services with strict QoS requirements, we need a novel data plane supporting high performance and flexible, fine granular programmability and control. As the network functions (implemented by virtual machines or containers) use the same hardware resources (cpu, memory) as the components responsible for networking, we need a low-level resource orchestrator which is capable of jointly controlling these resources. In this paper, we propose a novel resource orchestrator (RO) for a data plane making use of open source components such as, Docker, DPDK and OVS. Our goal is threefold. First, we propose a novel data plane resource model which is capable of abstracting several hardware architectures. Second, we provide an adapter module which can automatically discover the underlying hardware and build the model on-the-fly. Third, we design and implement a novel RO building on the aforementioned components and a publicly available Service Graph embedding engine. As a proof of the concept, two software switches (OVS, ERFS) are adapted and different hardware platforms are evaluated

This paper subverts the traditional understanding of Photoplethysmography (PPG) and opens up a new direction of the utility of PPG in commodity wearable devices, especially in the domain of human computer interaction of fine-grained gesture recognition. We demonstrate that it is possible to leverage the widely deployed PPG sensors in wrist-worn wearable devices to enable finger-level gesture recognition, which could facilitate many emerging human-computer interactions (e.g., sign-language interpretation and virtual reality). While prior solutions in gesture recognition require dedicated devices (e.g., video cameras or IR sensors) or leverage various signals in the environments (e.g., sound, RF or ambient light), this paper introduces the first PPG-based gesture recognition system that can differentiate fine-grained hand gestures at finger level using commodity wearables. Our innovative system harnesses the unique blood flow changes in a user's wrist area to distinguish the user's finger and hand movements. The insight is that hand gestures involve a series of muscle and tendon movements that compress the arterial geometry with different degrees, resulting in significant motion artifacts to the blood flow with different intensity and time duration. By leveraging the unique characteristics of the motion artifacts to PPG, our system can accurately extract the gesture-related signals from the significant background noise (i.e., pulses), and identify different minute finger-level gestures. Extensive experiments are conducted with over 3600 gestures collected from 10 adults. Our prototype study using two commodity PPG sensors can differentiate nine finger-level gestures from American Sign Language with an average recognition accuracy over 88%, suggesting that our PPG-based finger-level gesture recognition system is promising to be one of the most critical components in sign language translation using wearables.

The dominant application in today's Internet is content streaming, which is increasingly relying on caches to meet the stringent conditions on the latency between content servers and end-users. These systems routinely face the challenges of limited bandwidth capacities and network server failures, which degrade caching performance. In this paper, we study the problem of optimally allocating content over a resilient caching network, in which each cache may fail under some situations. Given content request rates and multiple routing paths, we formulate an optimization problem to maximize the expected caching gain, i.e., the reduction of latency due to intermediate caching. The offline version of this problem is NP-hard. We first propose a centralized, offline algorithm and show that a solution with (1-1/e) approximation ratio to the optimal can be constructed. We then propose a distributed ascent algorithm based on the concave relaxation of the expected gain. Informed by the results of our analysis, we finally propose a distributed resilient caching algorithm (DR-Cache) that is simple and adaptive to network failures. We show numerically that DR-Cache significantly outperforms other candidate algorithms under synthetic requests, as well as real world traces over a class of network topologies.

Cross-Technology Communication (CTC) is an enabling technology for efficient coexistence and effective cooperation among heterogeneous wireless devices by exchanging data frames directly without gateways. Recent advances in the physical-layer CTC achieve thousands of times faster speed than that of previous packet-level CTC techniques. However, physical-layer CTC still faces the challenge of inherent unreliability due to the imperfection of physical-layer signal emulation. Our work, named TwinBee, aims to recover the intrinsic errors of physical-layer CTC, by exploring chip-level error patterns. This is achieved interestingly without even observing the chip information and without any hardware modification. System evaluation shows that our key idea, namely symbol-level chip-combining decoding with soft mapping, significantly improves the Packet Reception Ratio (PRR) of the physical-layer CTC from 50%-60% to more than 99%. We also demonstrate the reliability of TwinBee in a data dissemination application over a network of 20 TelosB nodes, achieving over 40x reduction of data dissemination delay compared to Deluge.

Domain Name System (DNS) is one of the pillars of today's Internet. Due to its appealing properties such as low data volume, wide-ranging applications and encryption free, DNS traffic has been extensively utilized for network monitoring. Most existing studies of DNS traffic, however, focus on domain name reputation. Little attention has been paid to understanding and profiling what people are doing from DNS traffic, a fundamental problem in the areas including Internet demographics and network behavior analysis. Consequently, simple questions like “How to determine whether a DNS query for www.google.com means searching or any other behaviors?” cannot be answered by existing studies. In this paper, we take the first step to identify user activities from raw DNS queries. We advance a multiscale hierarchical framework to tackle two practical challenges, i.e., behavior ambiguity and behavior polymorphism. Under this framework, a series of novel methods, such as pattern upward mapping and multi-scale random forest classifier, are proposed to characterize and identify user activities of interest. Evaluation using both synthetic and real-world DNS traces demonstrates the effectiveness of our method.

Traffic measurement in high-speed networks has many applications in improving network performance, assisting resource allocation, and detecting anomalies. In this paper, we study a new problem called k-persistent spread estimation, which measures persist traffic elements in each flow that appear during at least k out of t measurement periods, where k and t can be arbitrarily defined in user queries. Solutions to this problem have interesting applications in network attack detection, popular content identification, user access profiling, etc. Yet, it is under-investigated as the prior work only addresses a special case with a questionable assumption. Designing an efficient and accurate k -persistent estimator requires us to use bitwise SUM (instead of bitwise AND typical in the prior art) to join the information collected from different periods. This seemly simple change has fundamental impact on the mathematical process in deriving an estimator, particular over space-saving virtual bitmaps. Based on real network traces, we show that our new estimator can accurately estimate the k -persistent spreads of the flows. It also performs much better than the existing work on the special case of measuring elements that appear in all periods.

Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based control framework, DRL-TE, for a fundamental networking problem: Traffic Engineering (TE). The proposed framework maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful Deep Neural Networks (DNNs). We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. To validate and evaluate the proposed framework, we implemented it in ns-3, and tested it comprehensively with both representative and randomly generated network topologies. Extensive packet-level simulation results show that 1) compared to several widely-used baseline methods, DRL-TE significantly reduces end-to-end delay and consistently improves the network utility, while offering better or comparable throughput; 2) DRL-TE is robust to network changes; and 3) DRL-TE consistently outperforms a state-of-the-art DRL method (for continuous control), Deep Deterministic Policy Gradient (DDPG), which, however, does not offer satisfying performance.

Privacy-preserving data publishing has been a heated research topic in the last decade. Numerous ingenious attacks on users' privacy and defensive measures have been proposed for the sharing of various data, varying from relational data, social network data, spatiotemporal data, to images and videos. Speech data publishing, however, is still untouched in the literature. To fill this gap, we study the privacy risk in speech data publishing and explore the possibilities of performing data sanitization to achieve privacy protection while preserving data utility simultaneously. We formulate this optimization problem in a general fashion and present thorough quantifications of privacy and utility. We analyze the sophisticated impacts of possible sanitization methods on privacy and utility, and also design a novel method - key term perturbation for speech content sanitization. A heuristic algorithm is proposed to personalize the sanitization for speakers to restrict their privacy leak (p-leak limit) while minimizing the utility loss. The simulations of linkage attacks and sanitization on real datasets validate the necessity and feasibility of this work.