Browsing by Author "BRESSAN, Stéphane"

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    ASSIST: Access Controlled Ship Identification Streams
    (2013-02-08T04:32:24Z) CAO, Jianneng; KISTER, Thomas; XIANG, Shili; MALHOTRA, Baljeet; TAN, Wee-Juan; TAN, Kian-Lee; BRESSAN, Stéphane
    The International Maritime Organization (IMO) requires a majority of cargo and passenger ships to use the Automatic Identification System (AIS) for navigation safety and traffic control. Distributing live AIS data on the Internet can offer a global view for both operational and analytical purposes to port authorities, shipping and insurance companies, cargo owners and ship captains and other stakeholders. Yet, uncontrolled, this distribution can seriously undermine navigation safety and security and the privacy of the various stakeholders. In this paper we present ASSIST, an application system based on our recently proposed access control framework, to protect streaming data from unauthorized access. Furthermore, we have implemented ASSIST on top of StreamInsight, a commercial stream engine. The extensive experimental results show that our solution is more effective and efficient than existing approaches.
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    Discretionary Social Network Data Revelation with a User-Centric Utility Guarantee
    (2012-08-08) YI, Song; KARRAS, Panagiotis; NOBARI, Sadegh; CHELIOTIS, Giorgos; XUE, Mingqiang; BRESSAN, Stéphane
    The proliferation of online social networks has created intense interest in studying the nature of such networks and revealing network information of interest to the end user. At the same time, the revelation of such data raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on what constitutes a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: instead of starting out with a privacy objective, we define a utility guarantee, in terms of certain graph connectivity properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the whole network graph, in which that user as well as distant connections and affliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real-world data, we demonstrate that our method preserves graph properties of interest (e.g., clustering coefficient, shortest path length, diameter, radius) more successfully than methods that randomly distort the graph to an equal extent, while it withstands structural attacks proposed in the literature.
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    Get a Sample for a Discount Sampling-Based XML Data Pricing
    (2014-03-12) TANG, Ruiming; AMARILLI, Antoine; SENELLART, Pierre; BRESSAN, Stéphane
    While price and data quality should define the major tradeoff for consumers in data markets, prices are usually prescribed by vendors and data quality is not negotiable. In this paper we study a model where data quality can be traded for a discount. We focus on the case of XML documents and consider completeness as the quality dimension. In our setting, the data provider offers an XML document, and sets both the price of the document and a weight to each node of the document, depending on its potential worth. The data consumer proposes a price. If the proposed price is lower than that of the entire document, then the data consumer receives a sample, i.e., a random rooted subtree of the document whose selection depends on the discounted price and the weight of nodes. By requesting several samples, the data consumer can iteratively explore the data in the document. We show that the uniform random sampling of a rooted subtree with prescribed weight is unfortunately intractable. However, we are able to identify several practical cases that are tractable. The first case is uniform random sampling of a rooted subtree with prescribed size; the second case restricts to binary weights. For both these practical cases we present polynomial-time algorithms and explain how they can be integrated into an iterative exploratory sampling approach.
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    Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines
    (2017-04-04) BASU, Debabrota; WANG, Xiayang; HONG, Yang; CHEN, Haibo; BRESSAN, Stéphane
    Cloud providers leverage live migration of virtual machines to reduce energy consumption and allocate resources efficiently in data centers. Each migration decision depends on three questions: when to move a virtual machine, which virtual machine to move and where to move it? Dynamic, uncertain and heterogeneous workloads running on virtual machines make such decisions difficult. Knowledge-based and heuristics-based algorithms are commonly used to tackle this problem. Knowledgebased algorithms, such as MaxWeight scheduling algorithms, are dependent on the specifics and the dynamics of the targeted Cloud architectures and applications. Heuristics-based algorithms, such as MMT algorithms, suffer from high variance and poor convergence because of their greedy approach. We propose a reinforcement learning approach. This approach does not require prior knowledge. It learns the dynamics of the workload as-itgoes. We formulate the problem of energy- and performance efficient resource management during live migration as a Markov decision process. While several learning algorithms are proposed to solve this problem, these algorithms remain confined to the academic realm as they face the curse of dimensionality. They are either not scalable in real-time, as it is the case of MadVM, or need an elaborate offline training, as it is the case of Q-learning. We propose an actor-critic algorithm, Megh, to overcome these deficiencies. Megh uses a novel dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space with a sparse basis. Megh has the capacity to learn uncertain dynamics and the ability to work in real-time. Megh is both scalable and robust. We implement Megh using the CloudSim toolkit and empirically evaluate its performance with the PlanetLab and the Google Cluster workloads. Experiments validate that Megh is more costeffective, incurs smaller execution overhead and is more scalable than MadVM and MMT. We explicate our choice of parameters through a sensitivity analysis.
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    A Stratified Approach to Progressive Approximate Joins
    (2007-09-24) TOK, Wee Hyong; BRESSAN, Stéphane; LEE, Mong-Li
    Users often do not require a complete answer to their query but rather only a sample. They expect the sample to be either the largest possible or the most representative (or both) given the resources available. We call the query processing techniques that deliver such results approximate. Process- ing of queries to streams of data is said to be progressive when it can continuously produce results as data arrives. In this paper, we are interested in the progressive and approxi- mate processing of queries to data streams when processing is limited to main memory. In particular, we study one of the main building blocks of such processing: the progressive approximate join. We devise and present several novel progressive approximate join algorithms. We empirically evaluate the performance of our algorithms and compare them with those of algorithms based on existing techniques. In particu- lar we study the trade-off between maximization throughput and maximization of representativeness of the sample.