234 research outputs found

    SybMatch: Sybil detection for privacy-preserving task matching in crowdsourcing

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    The past decade has witnessed the rise of crowdsourcing, and privacy in crowdsourcing has also gained rising concern in the meantime. In this paper, we focus on the privacy leaks and sybil attacks during the task matching, and propose a privacy-preserving task matching scheme, called SybMatch. The SybMatch scheme can simultaneously protect the privacy of publishers and subscribers against semi-honest crowdsourcing service provider, and meanwhile support the sybil detection against greedy subscribers and efficient user revocation. Detailed security analysis and thorough performance evaluation show that the SybMatch scheme is secure and efficient

    Anonymous privacy-preserving task matching in crowdsourcing

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    With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, but also achieves query traceability against dishonest or revoked workers. Detailed privacy analysis and thorough performance evaluation show that the proposed scheme is secure and feasible

    Effective and Secure Healthcare Machine Learning System with Explanations Based on High Quality Crowdsourcing Data

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    Affordable cloud computing technologies allow users to efficiently outsource, store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. With this exponential growth of the stored large scale clinical data and the growing need for personalized care, researchers are keen on developing data mining methodologies to learn efficient hidden patterns in such data. While studies have shown that those progresses can significantly improve the performance of various healthcare applications for clinical decision making and personalized medicine, the collected medical datasets are highly ambiguous and noisy. Thus, it is essential to develop a better tool for disease progression and survival rate predictions, where dataset needs to be cleaned before it is used for predictions and useful feature selection techniques need to be employed before prediction models can be constructed. In addition, having predictions without explanations prevent medical personnel and patients from adopting such healthcare deep learning models. Thus, any prediction models must come with some explanations. Finally, despite the efficiency of machine learning systems and their outstanding prediction performance, it is still a risk to reuse pre-trained models since most machine learning modules that are contributed and maintained by third parties lack proper checking to ensure that they are robust to various adversarial attacks. We need to design mechanisms for detection such attacks. In this thesis, we focus on addressing all the above issues: (i) Privacy Preserving Disease Treatment & Complication Prediction System (PDTCPS): A privacy-preserving disease treatment, complication prediction scheme (PDTCPS) is proposed, which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. (ii) Incentivizing High Quality Crowdsourcing Data For Disease Prediction: A new incentive model with individual rationality and platform profitability features is developed to encourage different hospitals to share high quality data so that better prediction models can be constructed. We also explore how data cleaning and feature selection techniques affect the performance of the prediction models. (iii) Explainable Deep Learning Based Medical Diagnostic System: A deep learning based medical diagnosis system (DL-MDS) is present which integrates heterogeneous medical data sources to produce better disease diagnosis with explanations for authorized users who submit their personalized health related queries. (iv) Attacks on RNN based Healthcare Learning Systems and Their Detection & Defense Mechanisms: Potential attacks on Recurrent Neural Network (RNN) based ML systems are identified and low-cost detection & defense schemes are designed to prevent such adversarial attacks. Finally, we conduct extensive experiments using both synthetic and real-world datasets to validate the feasibility and practicality of our proposed systems

    BMSQABSE: Design of a Bioinspired Model to Improve Security & QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

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    Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification & removal of storage & computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security & QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order & generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS & security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments

    Improving privacy preserving in modern applications

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    The thesis studies the privacy problems in various modern applications, such as recommendation system, Internet of Things, location-based service and crowdsourcing system. The corresponding solutions are proposed, and the proposed solutions not only protect the data privacy with guaranteed privacy level, but also enhancing the data utility

    Security and Privacy Preservation in Mobile Crowdsensing

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    Mobile crowdsensing (MCS) is a compelling paradigm that enables a crowd of individuals to cooperatively collect and share data to measure phenomena or record events of common interest using their mobile devices. Pairing with inherent mobility and intelligence, mobile users can collect, produce and upload large amounts of data to service providers based on crowdsensing tasks released by customers, ranging from general information, such as temperature, air quality and traffic condition, to more specialized data, such as recommended places, health condition and voting intentions. Compared with traditional sensor networks, MCS can support large-scale sensing applications, improve sensing data trustworthiness and reduce the cost on deploying expensive hardware or software to acquire high-quality data. Despite the appealing benefits, however, MCS is also confronted with a variety of security and privacy threats, which would impede its rapid development. Due to their own incentives and vulnerabilities of service providers, data security and user privacy are being put at risk. The corruption of sensing reports may directly affect crowdsensing results, and thereby mislead customers to make irrational decisions. Moreover, the content of crowdsensing tasks may expose the intention of customers, and the sensing reports might inadvertently reveal sensitive information about mobile users. Data encryption and anonymization techniques can provide straightforward solutions for data security and user privacy, but there are several issues, which are of significantly importance to make MCS practical. First of all, to enhance data trustworthiness, service providers need to recruit mobile users based on their personal information, such as preferences, mobility pattern and reputation, resulting in the privacy exposure to service providers. Secondly, it is inevitable to have replicate data in crowdsensing reports, which may possess large communication bandwidth, but traditional data encryption makes replicate data detection and deletion challenging. Thirdly, crowdsensed data analysis is essential to generate crowdsensing reports in MCS, but the correctness of crowdsensing results in the absence of malicious mobile users and service providers become a huge concern for customers. Finally yet importantly, even if user privacy is preserved during task allocation and data collection, it may still be exposed during reward distribution. It further discourage mobile users from task participation. In this thesis, we explore the approaches to resolve these challenges in MCS. Based on the architecture of MCS, we conduct our research with the focus on security and privacy protection without sacrificing data quality and users' enthusiasm. Specifically, the main contributions are, i) to enable privacy preservation and task allocation, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation. In SPOON, the service provider recruits mobile users based on their locations, and selects proper sensing reports according to their trust levels without invading user privacy. By utilizing the blind signature, sensing tasks are protected and reports are anonymized. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users; ii) to improve communication efficiency while guaranteeing data confidentiality, we propose a fog-assisted secure data deduplication scheme, in which a BLS-oblivious pseudo-random function is developed to enable fog nodes to detect and delete replicate data in sensing reports without exposing the content of reports. Considering the privacy leakages of mobile users who report the same data, the blind signature is utilized to hide users' identities, and chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous greedy mobile users; iii) to achieve data statistics with privacy preservation, we propose a privacy-preserving data statistics scheme to achieve end-to-end security and integrity protection, while enabling the aggregation of the collected data from multiple sources. The correctness verification is supported to prevent the corruption of the aggregate results during data transmission based on the homomorphic authenticator and the proxy re-signature. A privacy-preserving verifiable linear statistics mechanism is developed to realize the linear aggregation of multiple crowdsensed data from a same device and the verification on the correctness of aggregate results; and iv) to encourage mobile users to participating in sensing tasks, we propose a dual-anonymous reward distribution scheme to offer the incentive for mobile users and privacy protection for both customers and mobile users in MCS. Based on the dividable cash, a new reward sharing incentive mechanism is developed to encourage mobile users to participating in sensing tasks, and the randomization technique is leveraged to protect the identities of customers and mobile users during reward claim, distribution and deposit

    Privacy Enhancing Technologies for solving the privacy-personalization paradox : taxonomy and survey

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    Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users’ physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users’ privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent - economical and ethical - purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions

    Security and Privacy for Mobile Social Networks

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    With the ever-increasing demands of people's social interactions, traditional online social networking applications are being shifted to the mobile ones, enabling users' social networking and interactions anywhere anytime. Due to the portability and pervasiveness of mobile devices, such as smartphones, wearable devices and tablets, Mobile Social Network (MSN), as a promising social network platform, has become increasingly popular and brought immense benefits. In MSN, users can easily discover and chat with social friends in the vicinity even without the Internet; vehicle drivers and passengers can exchange traffic information, videos or images with other vehicles on the road; customers in a shopping mall can share sale information and recommend it to their friends. With MSNs, massive opportunities are created to facilitate people's social interactions and enlarge the inherent social circle. However, the flourish of MSNs also hinges upon fully understanding and managing the challenges, such as security threats and privacy leakage. Security and privacy concerns rise as the boom of MSN applications comes up, but few users have paid adequate attentions to protect their privacy-sensitive information from disclosing. First of all, to initiate social interactions, users sometimes exchange their social interests or preferences with each other (including strangers in the vicinity) without sufficient protections. As such, some private information may be inferred from the exchanged social interests by attackers and untrusted users. Secondly, some malicious attackers might forge fake identities or false contents, such as spam and advertisements, to disrupt MSNs or mislead other users. These attackers could even collude and launch a series of security threats to MSNs. In addition, massive social network data are usually stored in untrusted cloud servers, where data confidentiality, authentication, access control and privacy are of paramount importance. Last but not least, the trade-off between data availability and privacy should be taken into account when the data are stored, queried and processed for various MSN applications. Therefore, novel security and privacy techniques become essential for MSN to provide sufficient and adjustable protections. In this thesis, we focus on security and privacy for MSNs. Based on the MSN architecture and emerging applications, we first investigate security and privacy requirements for MSNs and introduce several challenging issues, i.e., spam, misbehaviors and privacy leakage. To tackle these problems, we propose efficient security and privacy preservation schemes for MSNs. Specifically, the main contributions of this thesis can be three-fold. Firstly, to address the issues of spam in autonomous MSNs, we propose a personalized fine-grained spam filtering scheme (PIF), which exploits social characteristics during data delivery. The PIF allows users to create personalized filters according to their social interests, and enables social friends to hold these filters, discarding the unwanted data before delivery. We also design privacy-preserving coarse-grained and fine-grained filtering mechanisms in the PIF to not only enable the filtering but also prevent users' private information included in the filters from disclosing to untrusted entities. Secondly, to detect misbehaviors during MSN data sharing, we propose a social-based mobile Sybil detection scheme (SMSD). The SMSD detects Sybil attackers by differentiating the abnormal pseudonym changing and contact behaviors, since Sybil attackers frequently or rapidly change their pseudonyms to cheat legitimate users. As the volume of contact data from users keeps increasing, the SMSD utilizes local cloud servers to store and process the users' contact data such that the burden of mobile users is alleviated. The SMSD also detects the collusion attacks and prevents user's data from malicious modification when employing the untrusted local cloud server for the detection. Thirdly, to achieve the trade-off between privacy and data availability, we investigate a centralized social network application, which exploits social network to enhance human-to-human infection analysis. We integrate social network data and health data to jointly analyze the instantaneous infectivity during human-to-human contact, and propose a novel privacy-preserving infection analysis approach (PIA). The PIA enables the collaboration among different cloud servers (i.e., social network cloud server and health cloud server). It employs a privacy-preserving data query method based on conditional oblivious transfer to enable data sharing and prevent data from disclosing to untrusted entities. A privacy-preserving classification-based infection analysis method is also proposed to enable the health cloud server to infer infection spread but preserve privacy simultaneously. Finally, we summarize the thesis and share several open research directions in MSNs. The developed security solutions and research results in this thesis should provide a useful step towards better understanding and implementing secure and privacy-preserving MSNs
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