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TextBugger: Generating Adversarial Text Against Real-world Applications
Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendousExpand
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SecGraph: A Uniform and Open-source Evaluation System for Graph Data Anonymization and De-anonymization
In this paper, we analyze and systematize the state-of-the-art graph data privacy and utility techniques. Specifically, we propose and develop SecGraph (available at [1]), a uniform and open-sourceExpand
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Structural Data De-anonymization: Quantification, Practice, and Implications
In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems inExpand
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Graph Data Anonymization, De-Anonymization Attacks, and De-Anonymizability Quantification: A Survey
Nowadays, many computer and communication systems generate graph data. Graph data span many different domains, ranging from online social network data from networks like Facebook to epidemiologicalExpand
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ARM: An asynchronous receiver-initiated multichannel MAC protocol with duty cycling for WSNs
This paper proposes ARM, an receiver-initiated MAC protocol with duty cycling to tackle control channel saturation, triple hidden terminal and low broadcast reliability problems in asynchronousExpand
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Interpretable Deep Learning under Fire
Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help users understand theExpand
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On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge
In this paper, we conduct the first comprehensive quantification on the perfect de-anonymizability and partial deanonymizability of real world social networks with seed information in generalExpand
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Constructing Load-Balanced Data Aggregation Trees in Probabilistic Wireless Sensor Networks
Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs).Expand
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Differentially Private Releasing via Deep Generative Model
Privacy-preserving releasing of complex data (e.g., image, text, audio) represents a long-standing challenge for the data mining research community. Due to rich semantics of the data and lack of aExpand
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SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems
Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave. InExpand
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