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检索条件"任意字段=Proceedings of the 5th ACM Conference on Data and Application Security and Privacy"

共 1519 条记录，以下是21-30 订阅

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IoTFuzzSentry: Hunting Bugs In the IoT Wilderness In Operational Phase Using Payload Fuzzing 24

IoTFuzzSentry: Hunting Bugs In The IoT Wilderness In Operati...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Chaudhary, Priyanka Rushikesh Krishna, N. Pranav Maiti, Rajib Ranjan BITS Pilani Hyderabad Campus Secunderabad India

ISBN: (纸本)9798400704215

In the operational phase, an IoT device runs a light-weight server that is responsible for responding to the user queries, like accessing video and taking a snap in a IoT camera. the flaws in the implementation of certain security mechanisms in these IoT devices can lead to varying level of security threats. In this paper, we address the problems of discovering such vulnerabilities using effective fuzzing via crafted transport and upper layer protocol packets in IoT communication. We have designed and developed a mutation-based fuzzing tool, named IoTFuzzSentry, to discover certain non-trivial vulnerabilities in commercial IoT devices and demonstrate their potential exploits. We have used IoT devices, like IP cameras and Smart Plug. We have categorized the vulnerabilities into four types (IoT Credential Leakage (V1), Sneak IP camera live video stream (V2), Creep Live IoT Image (V3), IoT Command Injection (V4)) and show their exploits using three IoT devices. We have reported all these vulnerabilities to respective vendors via email. We believe that our IoTFuzzSentry has a great potential to discover such unconventional security threats and allow IoT vendors to strengthen the security of their commercialized devices.

关键词： Mutation Fuzzing IoT Devices Vulnerability privacy

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CODASPY 2018 - proceedings of the 8th acm conference on data and application security and privacy

CODASPY 2018 - Proceedings of the 8th ACM Conference on Data...

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8th acm conference on data and application security and privacy, CODASPY 2018

ISBN: (纸本)9781450356329

the proceedings contain 45 papers. the topics discussed include: minimizing privilege assignment errors in cloud services;secure storage with replication and transparent deduplication;server-based manipulation attacks against machine learning models;smartprovenance: a distributed, blockchain based dataProvenance system;cross-app tracking via nearby Bluetooth low energy devices;privacy-preserving certification of sustainability metrics;capacity: an abstract model of control over personal data;remote attestation for low-end prover devices with post-quantum capabilities;IoTVerif: an automated tool to verify SSL/TLS certificate validation in Android MQTT client applications;keyboard emanations in remote voice calls: password leakage and noise(less) masking defenses;identifying relevant information cues for vulnerability assessment using CVSS;malware analysis of imaged binary samples by convolutional neural network with attention mechanism;automated generation of attack graphs using NVD;an empirical study of differentially-private analytics for high-speed network data;a low energy profile: analysing characteristic security on BLE peripherals;secure display for FIDO transaction confirmation;and SeCore:continuous extrospection with high visibility on multi-core ARM platforms.

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Coherent Multi-Table data Synthesis for Tabular and Time-Series data with GANs 24

Coherent Multi-Table Data Synthesis for Tabular and Time-Ser...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Elliker, Clement Tonnelier, Emeric Shabou, Aymen Grp Credit Agricole DataLab Grp Paris France

ISBN: (纸本)9798400704215

As the usage of user-private-data is increasingly monitored by regulatory institutions for security purposes, its transfer becomes more constrained. Synthetic data has recently emerged as a viable alternative to prevent the disclosure of user-protected information that complies with data sharing regulations. Both public and private sectors commonly use a combination of tabular and time-series tables that often contains user-related sensitive information. they are usually intrinsically interlinked as they describe the users and their behaviors over different perimeters. Moreover, it contains both numerical and categorical features, adding complexity to the anonymization task. State of the art generative methods, specialized either in tabular or time-series data, are able to generate high quality synthetic data. However, if each table is generated independently, it becomes impossible to link them. As a result, the usability of such synthetic data is impacted. To address this issue, we not only propose a coherent multi-table generative model that uses Generative Adversarial Networks (GANs) to sample both tabular and time-series tables, but also a conditional time-series generative model that handles both numerical and categorical features. Additionally, many experiments are conducted to analyse the inner modules of our model and evaluate it on an in-house private dataset in order to prove the viability of the synthetic data generated for machine learning tasks.

关键词： synthetic data generative ai gan tabular data time-series data deep learning data privacy

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CoCoT: Collaborative Contact Tracing 24

CoCoT: Collaborative Contact Tracing

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Kann, Trevor Bauer, Lujo Cunningham, Robert K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9798400704215

Contact tracing can limit the spread of infectious diseases by notifying people of potential exposure to disease. Manual contact tracing is resource-intensive, but much of it can be automated using mobile phones, which are ubiquitous and can detect and record nearby contacts. Two major problems arise with automated contact tracing (ACT): preventing abuse for mass surveillance and accurately determining contacts. For example, the most widely adopted solution-Google and Apple's Exposure Notification (GAEN)-protects user privacy but suffers from inaccurate distance measurements that result in poor risk assessments. We propose to use collaboration among nearby devices to increase distance estimation accuracy, and therefore risk assessment accuracy, while minimizing the loss of user privacy. Our protocol, CoCoT, extends GAEN, a proximity-based, distributed ACT protocol, by adding an additional broadcast to share locally-derived distance estimates. To evaluate CoCoT, we develop a method for merging phone sensor datasets with human interaction datasets to approximate realistic scenarios and test our protocol. CoCoT improves distance estimate accuracy by 28% over the current best distance estimators and we analytically show impact on privacy, security, and battery consumption are minimal.

关键词： Automatic Contact Tracing Distributed Optimization Network Localization Smartphone privacy

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ASTRA-5G: Automated Over-the-Air security Testing and Research Architecture for 5G SA Devices 24

ASTRA-5G: Automated Over-the-Air Security Testing and Resear...

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17th acm conference on security and privacy in Wireless and Mobile Networks (WiSec)

作者： Khandker, Syed Guerra, Michele Bitsikas, Evangelos Jover, Roger Piqueras Ranganathan, Aanjhan Popper, Christina New York Univ Abu Dhabi Abu Dhabi U Arab Emirates Univ Molise Pesche Italy Northeastern Univ Boston MA USA Google New York NY USA

ISBN: (纸本)9798400705823

Despite the widespread deployment of 5G technologies, there exists a critical gap in security testing for 5G Standalone (SA) devices. Existing methods, largely manual and labor-intensive, are ill-equipped to fully uncover the state of security in the implementations of 5G SA protocols and standards on devices, severely limiting the ability to conduct comprehensive evaluations. To address this issue, in this work, we introduce a novel, open-source framework that automates the security testing process for 5G SA devices. By leveraging enhanced functionalities of 5G SA core and Radio Access Network (RAN) software, our framework offers a streamlined approach to generating, executing, and evaluating test cases, specifically focusing on the Non-Access Stratum layer. Our application of this framework across multiple 5G SA devices provides in-depth security insights, significantly improving testing efficiency and breadth.

关键词： 5G security Automated Testing Open5GS srsRAN

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An Overview of threats Exploring the Confusion Between Top-Level Domains and File Type Extensions 24

An Overview of Threats Exploring the Confusion Between Top-L...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Sales, Anderson Torres, Nuno Pinto, Pedro Inst Politecn Viana do Castelo Viana Do Castelo Portugal INESC TEC Porto Portugal

ISBN: (纸本)9798400704215

Cyberattacks exploit deceptions involving the Domain Name Service (DNS) to direct users to fake websites, such as typosquatting attacks, which exploit natural typographical errors, and homograph attacks, where different Unicode characters resemble the legitimate ones. the deception attacks may also exploit the confusion between DNS domain names, specifically Top-Level Domains (TLDs), and file extensions. Recently, two new TLDs were added, "zip" and "mov", sharing names with certain file types. this overlapping can be explored by malicious actors in a range of threat scenarios to compromise user security. this paper provides an overview of threats originating from the confusion between specific TLDs and file extensions, such as the recent "zip" and "mov". the threats are grouped into 6 threat scenarios that are described and discussed. this research can be part of a more comprehensive strategy that includes addressing the risks associated with these threats and designing future strategies to address the threats associated with exploiting this ambiguity.

关键词： Top-Level Domains File Extensions security threats Cyber security Cyber Attack

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Towards Accurate and Stronger Local Differential privacy for Federated Learning with Staircase Randomized Response 24

Towards Accurate and Stronger Local Differential Privacy for...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Varun, Matta Feng, Shuya Wang, Han Sural, Shamik Hong, Yuan Indian Inst Technol Kharagpur India Univ Connecticut Storrs CT USA Univ Kansas Lawrence KS USA

ISBN: (纸本)9798400704215

Federated Learning (FL), a privacy-preserving training approach, has proven to be effective, yet its vulnerability to attacks that extract information from model weights is widely recognized. To address such privacy concerns, Local Differential privacy (LDP) has been applied to FL: perturbing the weights trained for the local model by each client. However, besides high utility loss on the randomized model weights, we identify a new inference attack to the existing LDP method, that can reconstruct the original value from the noisy values with high confidence. To mitigate these issues, in this paper, we propose the Staircase Randomized Response (SRR)-FL framework, which assigns higher probabilities to weights closer to the true weight, reducing the distance between the true and perturbed data. this minimizes the noise for maintaining the same LDP guarantee, leading to better utility. Compared to existing LDP mechanisms (e.g., Generalized Randomized Response) on the FL, SRR-FL can further provide a more accurate privacy-preserving training model, and enhance the robustness against the inference attack while ensuring the same LDP guarantee. Furthermore, we also use the parameter shuffling method for privacy amplification. the efficacy of SRR-FL has been validated on widely used datasets MNIST, Medical-MNIST and CIFAR-10, demonstrating remarkable performance.

关键词： Local differential privacy federated learning client-level LDP

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Crypto'Graph: Leveraging privacy-Preserving Distributed Link Prediction for Robust Graph Learning 24

Crypto'Graph: Leveraging Privacy-Preserving Distributed Link...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Azogagh, Sofiane Birba, Zelma Aubin Gambs, Sebastien Killijian, Marc-Olivier Univ Quebec Montreal Montreal PQ Canada

ISBN: (纸本)9798400704215

Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still be willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private graph of each party, even though they know the number of nodes they have, since they share the same graph in terms of nodes but not the same links. Crypto'Graph improves on previous works by enabling the computation of a diverse set of similarity metrics in parallel without any additional cost. the use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which potential adversarial links are identified without compromising the privacy of the graphs of individual parties. the effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a node classification task using graph neural networks is demonstrated through extensive experimentation on two real-world datasets.

关键词： security privacy secure multiparty computation graph

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security Policy Generation and Verification through Large Language Models: A proposal 24

Security Policy Generation and Verification through Large La...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Martinelli, Fabio Mercaldo, Francesco Petrillo, Luca Santone, Antonella CNR IIT Pisa Italy Univ Molise Campobasso Italy CNR IIT Campobasso Italy IMT Sch Adv Studies Lucca Lucca Italy CNR IIT Lucca Italy

ISBN: (纸本)9798400704215

the ability to manage and enforce security policies in a rapidly changing digital environment is vital. When enforcing and verifying policies on complex systems, it can be challenging for them to be understood by humans. Furthermore, updating access control rules or configurations that are translated from natural language documents might take considerable time and eventually cause errors and damage. It is crucial for the rules and configurations to be modified with care in order to prevent such mistakes. the aim of the following proposal is the design of a method to convert security policies written in natural language into a machine-understandable format that can easily be enforced, verified as well and understood by machines. To this end, we propose using advanced policy languages like XacmL and Rego to translate security policies into formats that are automatically enforceable and verifiable by the system. In addition, this transformation will facilitate the implementation of security policies within modern application architectures including micro-services and cloud-native environments. therefore, under its auspices, a framework that eases policy enforcement and minimizes human errors while ensuring conformity to safety standards set out shall be born out of this research proposal.

关键词： security policy machine learning testing

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Image data confirmation method based on security watermarking and blockchain 24

Image data confirmation method based on security watermarkin...

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5th International conference on Computing, Networks and Internet of things (CNIOT)

作者： Zhang, Liu Du, Xue-Hui Liu, Ao-Di Informat Engn Univ He Nan Prov Key Lab Informat Secur Zhengzhou Peoples R China

ISBN: (纸本)9798400716751

Aiming at the problem of unclear resource ownership and difficult definition of image data security sharing technology, combined with blind watermarking technology, this paper proposes a method of image data rights confirmation based on security watermarking and block chain to realize the security of image data rights confirmation in the flow process and ensure the integrity of the image. Firstly, SM2 digital signature algorithm is used to sign the identity information of image data holder to realize the identity authentication of data. At the same time, SM4 is used to encrypt the relevant information to realize the encrypted transmission of the information in the watermark. Upload the hash value of the watermark to the blockchain to ensure that the watermark information cannot be tampered with;then, the watermark embedding and extraction algorithm based on discrete wavelet transform and singular value decomposition (DWT-SVD) is used to ensure the concealment of the information flow. Finally, compared with the existing right confirmation methods, it is verified that the proposed method has certain advantages of security and concealment, and can meet the needs of privacy protection and traceability.

关键词： data validation Blockchain Blind watermarking

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