Pengzhan Wang

Private Weighted Histogram Aggregation in Crowdsourcing

Histogram is one of the fundamental aggregates in crowdsourcing data aggregation. In a crowdsourcing aggregation task, the potential value or importance of each bucket in the histogram may differs, especially when the number of buckets is relatively large but only a few of buckets are of great interests. This is the case weighted histogram aggregation is needed. On the other hand, privacy is a critical issue in crowdsourcing, as data contributed by participants may reveal sensitive information about individuals. In this paper, we study the problem of privacy-preserving weighted histogram aggregation, and propose a new local differential-private mechanism, the bi-parties mechanism, which exploits the weight imbalances among buckets in histogram to minimize weighted error. We provide both theoretical and experimental analyses of the mechanism, specifically, the experimental results demonstrate that our mechanism can averagely reduce \(20\,\%\) of weighted square error of estimated histograms compared to existing approaches (e.g. randomized response mechanism, exponential mechanism).

Recognizing the Operating Hand from Touchscreen Traces on Smartphones

As the size of smartphone touchscreens becomes larger and larger in recent years, operability with single hand is getting worse especially for female users. We envision that user experience can be significantly improved if smartphones are able to detect the current operating hand and adjust the UI subsequently. In this paper, we propose a novel scheme that leverages user-generated touchscreen traces to recognize current operating hand accurately, with the help of a supervised classifier constructed from twelve different kinds of touchscreen trace features. As opposed to existing solutions that all require users to select the current operating hand or dominant hand manually, our scheme follows a more convenient and practical manner, and allows users to change operating hand frequently without any harm to user experience. We conduct a series of real-world experiments on Samsung Galaxy S4 smartphones, and evaluation results demonstrate that our proposed approach achieves 94.1% accuracy when deciding with a single trace only, and the false positive rate is as low as 2.6%.

Privacy preserving big histogram aggregation for spatial crowdsensing

The popularity of mobile devices has far expanded the application scenarios of spatial crowdsensing, due to its ability to provide fine-grained multi dimensional sensor readings associated with location information. Privacy is one of the fundamental issues in crowdsensing, as these location-based sensor readings may reveal identities or activities of participants. In this paper, we adopts the state-of-art location privacy definition geo-indistinguishability, provide an efficient and effective privacy preserving histogram aggregation mechanism BFMM (Bit Flipping Matrix Mechanism) for fine-grained multi dimensional location-based data. Theoretical analyses and experimental results demonstrate the efficiency and effectiveness of our approach for fine-grained multidimensional location-based data. Specifically, the aggregation accuracy of our approach averagely outperforms existing methods by a factor of number of buckets in the histogram.

Deploying default paths by joint optimization of flow table and group table in SDNs

Software Defined Networking (SDN) separates the control plane from the data plane to ease network management and provide flexibility in packet routing. The control plane interacts with the data plane through the forwarding tables, usually including a flow table and a group table, at each switch. Due to high cost and power consumption of Ternary Content Addressable Memory (TCAM), commodity switches can only support flow/group tables of limited size, which presents serious challenge for SDN to scale to large networks. One promising approach to address the scalability problem is to deploy aggregate default paths specified by wildcard forwarding rules. However, the multi-dimensional interaction among numerous system parameters and performance/scalability considerations makes the problem of setting up the flow/group tables at all switches for optimal overall layout of default paths very challenging. This paper studies the joint optimization of flow/group tables in the complex setting of large-scale SDNs. We formulate this problem as an integer linear program, and prove its NP-Hardness. An efficient algorithm with bounded approximation factors is proposed to solve the problem. The properties of our algorithm are formally analyzed. We implement the proposed algorithm on an SDN testbed for experimental studies and use simulations for large-scale investigation. The experimental results and simulation results demonstrate high efficiency of our proposed algorithm.

On the effect of flow table size and controller capacity on SDN network throughput

Software Defined Network (SDN) is an architectural trend in networking towards the use of the centralized controller to get better performance. However, due to limited resources (especially limited flow table size and controller processing capacity), it may result in low-throughput and long-delay for a set of bursty flows. In this paper, we first combine the flow table size constraint and the controller processing capacity constraint to define the Throughput Maximization with Limited Resources (TMLR) problem. Then we prove TMLR is NP-Hard and design an approximation algorithm to solve the TMLR problem. The approximation factor of the proposed algorithm is also analyzed. The simulation results on the SDN platform (Mininet [1]) show that our algorithm can improve the network throughput about 39% on average compared with the existing algorithms.

Control Link Load Balancing and Low Delay Route Deployment for Software Defined Networks

Software defined networking (SDN) separates the data plane and control plane on independent devices. Since the data plane, consisting of switches, is responsible for packets forwarding, previous work often considers the different constraints (e.g., data link capacity and flow-table size) only in the data plane to provide better QoS for users. However, due to limited CPU processing power and low speed of flow-table updating on each switch, the control channels/links between switches and the controller often have very limited capacity, which will cause QoS performance (e.g., response time and throughput) degradation when the switch should handle a high traffic load. The goal of our paper is to achieve better QoS by jointly considering the control link constraint and other different constraints of the data plane in SDNs. We formally define the control link load balancing and low delay route deployment problems, and prove the NP-Hardness. We present two algorithms with bounded approximation factors for each problem and implement the proposed methods on our SDN testbed. Extensive simulation results and experimental results show that our algorithms can reduce control link load by about 50% and response time by about 60%, and increase the network throughput by 65% compared with previous methods.

Rule Anomalies Detecting and Resolving for Software Defined Networks

Software Defined Network (SDN) is facilitating rapid innovation of network by providing a programmable network infrastructure. However, managing SDN flow rules, especially among multiple modules and administrators, has become complex and error-prone. Different controller modules with diverse objectives may be installed on the SDN controller, which can lead to anomalies among policies and rules. In this paper, we propose ADRS(Anomaly Detecting and Resolving for SDN) to solve this problem. Firstly, we analyse the rule-level anomalies that may occur in SDN based on OpenFlow protocol. Then we present an interval tree model for rapid rule scanning and a share model for network privilege allocating. By applying these models, we provide an automatic algorithm to detect and resolve the anomalies among SDN modules. Moreover, a rule-recovery mechanism is presented to avoid modification faults. We also implement and evaluate our system in the OpenDayLight controller.