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检索条件"任意字段=10th ACM Conference on Data and Application Security and Privacy, CODASPY 2020"

共 54 条记录，以下是21-30 订阅

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the Good, the Bad and the (Not So) Ugly of Out-Of-Band Authentication with eID Cards and Push Notifications 20

The Good, the Bad and the (Not So) Ugly of Out-Of-Band Authe...

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10th acm conference on data and application security and privacy (codaspy)

作者： Pernpruner, Marco Carbone, Roberto Ranise, Silvio Sciarretta, Giada Fdn Bruno Kessler Secur & Trust Res Unit Trento Italy

ISBN: (纸本)9781450371070

Everyday life is permeated by new technologies allowing people to perform almost any kind of operation from their smart devices. Although this is amazing from a convenience perspective, it may result in several security issues concerning the need for authenticating users in a proper and secure way. Electronic identity cards (also called eID cards) play a very important role in this regard, due to the high level of assurance they provide in identification and authentication processes. However, authentication solutions relying on them are still uncommon and suffer from many usability limitations. In this paper, we thus present the design and implementation of a novel passwordless, multi-factor authentication protocol based on eID cards. To reduce known usability issues while keeping a high level of security, our protocol leverages push notifications and mobile devices equipped with NFC, which can be used to interact with eID cards. In addition, we evaluate the security of the protocol through a formal security analysis and a risk analysis, whose results emphasize the acceptable level of security.

关键词： eID cards Formal analysis Multi-factor authentication Passwordless Push notifications Risk analysis security protocols

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PREDICT: Efficient Private Disease Susceptibility Testing in Direct-to-Consumer Model 20

PREDICT: Efficient Private Disease Susceptibility Testing in...

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10th acm conference on data and application security and privacy (codaspy)

作者： Ugwuoke, Chibuike Erkin, Zekeriya Reinders, Marcel Lagendijk, Reginald L. Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450371070

Genome sequencing has rapidly advanced in the last decade, making it easier for anyone to obtain digital genomes at low costs from companies such as Helix, MyHeritage, and 23andMe. Companies now offer their services in a direct-to-consumer (DTC) model without the intervention of a medical institution. thereby, providing people with direct services for paternity testing, ancestry testing and disease susceptibility testing (DST) to infer diseases' predisposition. Genome analyses are partly motivated by curiosity and people often want to partake without fear of privacy invasion. Existing privacy protection solutions for DST adopt cryptographic techniques to protect the genome of a patient from the party responsible for computing the analysis. Said techniques include homomorphic encryption, which can be computationally expensive and could take minutes for only a few single-nucleotide polymorphisms (SNPs). A predominant approach is a solution that computes DST over encrypted data, but the design depends on a medical unit and exposes test results of patients to the medical unit, making the design uncomfortable for privacy-aware individuals. Hence it is pertinent to have an efficient privacy-preserving DST solution with a DTC service. We propose a novel DTC model that protects the privacy of SNPs and prevents leakage of test results to any other party save for the genome owner. Conversely, we protect the privacy of the algorithms or trade secrets used by the genome analyzing companies. Our work utilizes a secure obfuscation technique in computing DST, eliminating expensive computations over encrypted data. Our approach significantly outperforms existing state-of-the-art solutions in runtime and scales linearly for equivalent levels of security. As an example, computing DST for 10,000 SNPs requires approximately 96 milliseconds on commodity hardware. With this efficient and privacy-preserving solution which is also simulation-based secure, we open possibilities for performi

关键词： SNP Genome privacy-Preserving Obfuscation Direct-to-customer Disease Susceptibility Testing

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SeCaS: Secure Capability Sharing Framework for IoT Devices in a Structured P2P Network 20

SeCaS: Secure Capability Sharing Framework for IoT Devices i...

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10th acm conference on data and application security and privacy (codaspy)

作者： Aktypi, Angeliki Kalkan, Kubra Rasmussen, Kasper B. Univ Oxford Dept Comp Sci Oxford England Ozyegin Univ Dept Comp Sci Istanbul Turkiye

ISBN: (纸本)9781450371070

the emergence of the internet of things (IoT) has resulted in the possession of a continuously increasing number of highly heterogeneous connected devices by the same owner. To make full use of the potential of a personal IoT network, there must be secure and effective cooperation between them. While application platforms (e.g., Samsung Smartthings) and interoperable protocols (e.g., MQTT) exist already, the reliance on a central hub to coordinate communication introduces a single-point of failure, provokes bottleneck problems and raises privacy concerns. In this paper we propose SeCaS, a Secure Capability Sharing framework, built on top of a peer-to-peer (P2P) architecture. SeCaS addresses the problems of fault tolerance, scalability and security in resource discovery and sharing for IoT infrastructures using a structured P2P network, in order to take advantage of the self-organised and decentralised communication it provides. SeCaS brings three main contributions: (i) a capability representation that allows each device to specify what services they offer, and can be used as a common language to search for, and exchange, capabilities, resulting in flexible service discovery that can leverage the properties on a distributed hash table (DHT);(ii) a set of four protocols that provides identification of the different devices that exist in the network and authenticity of the messages that are exchanged among them;and (iii) a thorough security and complexity analysis of the proposed scheme that shows SeCaS to be both secure and scalable.

关键词： IoT DHT Resource Sharing Scalability Fault-Tolerance privacy

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Ensuring privacy and security of IoT Networks Utilizing Blockchain and Federated Learning 10

Ensuring Privacy and Security of IoT Networks Utilizing Bloc...

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10th International conference on Future Internet of things and Cloud, FiCloud 2023

作者： Rashid, Md Mamunur Choi, Piljoo Lee, Suk-Hwan Platos, Jan Huh, Young Kwon, Ki-Ryong Pukyong National University Department of Artificial Intelligence Convergence Busan Korea Republic of Donga University Department of Computer Engineering Busan Korea Republic of VSB-Technical University of Ostrava Department of Electrical Engineering and Computer Science Ostrava Czech Republic Promotion Foundation Korea Smart Medical Device Industry Seoul Korea Republic of

ISBN: (纸本)9798350316353

the Internet of things (IoT) has become a game-changing technology, bridging the gap between the real and virtual worlds and allowing for smooth data transfer and communication between linked objects. the other two potential technologies are blockchain (BC) and artificial intelligence (AI), whose application areas are incredibly diverse and which may perform best when combined. Since some traditional machine learning (ML) techniques have limitations, this article proposed using distributed machine learning techniques such as federated learning and blockchain to build a more reliable and secure IoT network that will be better protected and less susceptible to outside intrusions. As an alternative to centralized cloud storage, we also recommended using decentralized data storage techniques like the InterPlanetary File System (IPFS) and Hyperledger Fabric (HLF). Additionally, as a proof of concept, we deployed our model using Ethereum Smart Contracts (SC). Using the well-known cybersecurity dataset known as Edge-IIoTset, we utilized both centralized and federated machine learning models to evaluate the efficiency of the suggested approach. the experimental results and successful deployment of Smart Contracts demonstrate that employing Blockchain and distributed storage systems is preferable for safeguarding IoT networks. © 2023 IEEE.

关键词： Blockchain

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TrustAV: Practical and privacy-Preserving Malware Analysis in the Cloud 20

TrustAV: Practical and Privacy-Preserving Malware Analysis i...

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10th acm conference on data and application security and privacy (codaspy)

作者： Deyannis, Dimitris Papadogiannaki, Eva Kalivianakis, Giorgos Vasiliadis, Giorgos Ioannidis, Sotiris FORTH ICS Iraklion Greece Univ Crete Dept Comp Sci Rethimnon Crete Greece

ISBN: (纸本)9781450371070

While the number of connected devices is constantly growing, we observe an increased incident rate of cyber attacks that target user data. Typically, personal devices contain the most sensitive information regarding their users, so there is no doubt that they can be a very valuable target for adversaries. Typical defense solution to safeguard user devices and data, are based in malware analysis mechanisms. To amortize the processing and maintenance overheads, the outsourcing of network inspection mechanisms to the cloud has become very popular recently. However, the majority of such cloud-based applications usually offers limited privacy preserving guarantees for data processing in third-party environments. In this work, we propose TrustAV, a practical cloud-based malware detection solution destined for a plethora of device types. TrustAV is able to offload the processing of malware analysis to a remote server, where it is executed entirely inside, hardware supported, secure enclaves. By doing so, TrustAV is capable to shield the transfer and processing of user data even in untrusted environments with tolerable performance overheads, ensuring that private user data are never exposed to malicious entities or honestbut-curious providers. TrustAV also utilizes various techniques in order to overcome performance overheads, introduced by the Intel SGX technology, and reduce the required enclave memory -a limiting factor for malware analysis executed in secure enclave environments- offering up to 3x better performance.

关键词： Intel SGX trusted execution environment antivirus data processing mobile privacy

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A Hypothesis Testing Approach to Sharing Logs with Confidence 20

A Hypothesis Testing Approach to Sharing Logs with Confidenc...

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10th acm conference on data and application security and privacy (codaspy)

作者： Long, Yunhui Xu, Le Gunter, Carl A. Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781450371070

Logs generated by systems and applications contain a wide variety of heterogeneous information that is important for performance profiling, failure detection, and security analysis. there is a strong need for sharing the logs among different parties to outsource the analysis or to improve system and security research. However, sharing logs may inadvertently leak confidential or proprietary information. Besides sensitive information that is directly saved in logs, such as user-identifiers and software versions, indirect evidence like performance metrics can also lead to the leakage of sensitive information about the physical machines and the system. In this work, we introduce a game-based definition of the risk of exposing sensitive information through released logs. We propose log indistinguishability, a property that is met only when the logs leak little information about the protected sensitive attributes. We design an end-to-end framework that allows a user to identify risk of information leakage in logs, to protect the exposure with log redaction and obfuscation, and to release the logs with a much lower risk of exposing the sensitive attribute. Our framework contains a set of statistical tests to identify violations of the log indistinguishability property and a variety of obfuscation methods to prevent the leakage of sensitive information. the framework views the log-generating process as a black-box and can therefore be applied to different systems and processes. We perform case studies on two different types of log datasets: Spark event log and hardware counters. We show that our framework is effective in preventing the leakage of the sensitive attribute with a reasonable testing time and an acceptable utility loss in logs.

关键词： indistinguishability hypothesis test log obfuscation privacy

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Can AI be for Good in the Midst of Cyber Attacks and privacy Violations?: A Position Paper 10

Can AI be for Good in the Midst of Cyber Attacks and Privacy...

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10th acm conference on data and application security and privacy, codaspy 2020

作者： thuraisingham, Bhavani M. University of Texas at Dallas Richardson TX United States

ISBN: (纸本)9781450371070

Artificial Intelligence (AI) is affecting every aspect of our lives from healthcare to finance to driving to managing the home. Sophisticated machine learning techniques with a focus on deep learning are being applied successfully to detect cancer, to make the best choices for investments, to determine the most suitable routes for driving as well as to efficiently manage the electricity in our homes. We expect AI to have even more influence as advances are made with technology as well as in learning, planning, reasoning and explainable systems. While these advances will greatly advance humanity, organizations such as the United Nations have embarked on initiatives such as "AI for Good" and we can expect to see more emphasis on applying AI for the good of humanity especially in developing countries. However, the question that needs to be answered is Can AI be for Good when when the AI techniques can be attacked and the AI techniques themselves can cause privacy violations? this position paper will provide an overview of this topic with protecting children and children's rights as an example. © 2020 Owner/Author.

关键词： adversarial machine learning ai for good children's rights cyber attacks privacy protection

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Explore the Transformation Space for Adversarial Images 20

Explore the Transformation Space for Adversarial Images

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10th acm conference on data and application security and privacy (codaspy)

作者： Chen, Jiyu Wang, David Chen, Hao Univ Calif Davis Davis CA 95616 USA Vestavia Hills High Sch Vestavia Hills AL USA

ISBN: (纸本)9781450371070

Deep learning models are vulnerable to adversarial examples. Most of current adversarial attacks add pixel-wise perturbations restricted to some L-p -norm, and defense models are evaluated also on adversarial examples restricted inside L-p-norm balls. However, we wish to explore adversarial examples exist beyond L-p-norm balls and their implications for attacks and defenses. In this paper, we focus on adversarial images generated by transformations. We start with color transformation and propose two gradient-based attacks. Since L-p-norm is inappropriate for measuring image quality in the transformation space, we use the similarity between transformations and the Structural Similarity Index. Next, we explore a larger transformation space consisting of combinations of color and affine transformations. We evaluate our transformation attacks on three data sets - CIFAR10, SVHN, and ImageNet - and their corresponding models. Finally, we perform retraining defenses to evaluate the strength of our attacks. the results show that transformation attacks are powerful. they find high-quality adversarial images that have higher transferability and misclassification rates than C&W's L-p attacks, especially at high confidence levels. they are also significantly harder to defend against by retraining than C&W's L-p attacks. More importantly, exploring different attack spaces makes it more challenging to train a universally robust model.

关键词： Adversarial attacks Image transformation Deep learning security

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N-m-Variant Systems: Adversarial-Resistant Software Rejuvenation for Cloud-Based Web applications 10

N-m-Variant Systems: Adversarial-Resistant Software Rejuvena...

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10th acm conference on data and application security and privacy, codaspy 2020

作者： Polinsky, Isaac Martin, Kyle Enck, William Reiter, Michael K. North Carolina State University Raleigh NC United States University of North Carolina at Chapel Hill Chapel Hill NC United States

ISBN: (纸本)9781450371070

Web servers are a popular target for adversaries as they are publicly accessible and often vulnerable to compromise. Compromises can go unnoticed for months, if not years, and recovery often involves a complete system rebuild. In this paper, we propose n-m-Variant Systems, an adversarial-resistant software rejuvenation framework for cloud-based web applications. We improve the state-of-the-art by introducing a variable m that provides a knob for administrators to tune an environment to balance resource usage, performance overhead, and security guarantees. Using m, security guarantees can be tuned for seconds, minutes, days, or complete resistance. We design and implement an n-m-Variant System prototype to protect a Mediawiki PHP application serving dynamic content from an external SQL persistent storage. Our performance evaluation shows a throughput reduction of 65% for 108 seconds of resistance and 83% for 12 days of resistance to sophisticated adversaries, given appropriate resource allocation. Furthermore, we use theoretical analysis and simulation to characterize the impact of system parameters on resilience to adversaries. through these efforts, our work demonstrates how properties of cloud-based servers can enhance the integrity of Web servers. © 2020 acm.

关键词： cloud security intrusion resilience n-variant systems

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10th EAI International conference on Big data Technologies and applications, BDTA 2020 and 13th EAI International conference on Wireless Internet, WiCON 2020

10th EAI International Conference on Big Data Technologies a...

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10th EAI International conference on Big data Technologies and applications, BDTA 2020 and 13th EAI International conference on Wireless Internet, WiCON 2020

ISBN: (纸本)9783030728014

the proceedings contain 14 papers. the special focus in this conference is on Big data Technologies and applications. the topics include: Performance Evaluation of Energy Detection, Matched Filtering and KNN Under Different Noise Models;hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition;research on User privacy security of China’s Top Ten Online Game Platforms;spectrum Sensing and Prediction for 5G Radio;towards Preventing Neighborhood Attacks: Proposal of a New Anonymization’s Approach for Social Networks data;early Detecting the At-risk Students in Online Courses Based on their Behavior Sequences;Do College Students Adapt to Personal Learning Environment (PLE)? A Single-Group Study;a Big data Intelligence Marketplace and Secure Analytics Experimentation Platform for the Aviation Industry;a Multi-valued Logic Assessment of Organizational Performance via Workforce Social Networking;Research on the Sharing and application of TCM Digital Resources;statistical Research on Macroeconomic Big data: Using a Bayesian Stochastic Volatility Model;introducing and Benchmarking a One-Shot Learning Gesture Recognition dataset.

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