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

共 561 条记录，以下是151-160 订阅

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Using Venom to Flip the Coin and Peel the Onion: Measurement Tool and dataset for Studying the Bitcoin - Dark Web Synergy: data/Toolset Paper 25

Using Venom to Flip the Coin and Peel the Onion: Measurement...

引用

proceedings of the Fifteenth acm conference on data and application security and privacy

作者： Lukas Ingmarsson Karl Duckert Karlsson Niklas Carlsson Linköping University Linköping Sweden

ISBN: (纸本)9798400714764

Bitcoin and the Dark Web present an interesting synergy that enables both legitimate anonymity and illicit activities, making it an important landscape to understand, especially as the Dark Web, with its hidden services, relies heavily on Bitcoin as a pseudonymous currency for transactions. However, a lack of scalable tools and timely datasets has limited systematic analysis of this ecosystem. To address this gap, we introduce Venom, a scalable framework for mapping Bitcoin activity on the Dark Web. Venom integrates multithreaded crawling, data extraction, and dataset generation, resulting in a comprehensive resource that allows us to easily collect snapshots of over 177,000 onion sites in roughly 24 hours. With the paper, we share both the tool and an example snapshot containing both per-site metadata and Bitcoin transaction data. Preliminary analysis reveals concentrated activity among key players and widespread content mirroring, offering new insights into the Dark Web's economic structure. Venom provides a critical resource for advancing research and monitoring in this domain.

关键词： Bitcoin

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Formal Analysis of ReBAC Policy Mining Feasibility 21

Formal Analysis of ReBAC Policy Mining Feasibility

引用

11th acm conference on data and application security and privacy (CODASPY)

作者： Chakraborty, Shuvra Sandhu, Ravi Univ Texas San Antonio Inst Cyber Secur ICS San Antonio TX 78249 USA Univ Texas San Antonio NSF Ctr Secur & Privacy Enhanced Cloud Comp C SPE Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9781450381437

Relationship-Based Access Control (ReBAC) expresses authorization in terms of various direct and indirect relationships amongst entities, most commonly between users. The need for ReBAC policy mining arises when an existing access control system is reformulated in ReBAC. This paper considers the feasibility of ReBAC policy mining in context of user to user authorization, such as arises in various social and business contexts. In accordance with the policy mining literature, we assume that complete data is provided regarding user to user authorizations for a given user set, along with complete relationship data amongst these users comprising a labeled relationship graph. A ReBAC policy language is also specified. ReBAC policy mining seeks to formulate a ReBAC policy with the given policy language and relationship graph, which is exactly equivalent to the given authorizations. ReBAC policy mining feasibility problem asks whether such a policy exists and if so to provide the policy. We investigate this problem in context of different ReBAC policy languages which differ in the relationships, inverse relationships and non-relationships that can be used to build the policy. We develop a feasibility detection algorithm and analyze its complexity. We show that our policy languages are progressively more expressive as we introduce additional capability. In case of infeasibility, various solution approaches are discussed.

关键词： Access control Policy mining Relationship-Based Access Control

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Padding Matters - Exploring Function Detection in PE Files: data/Toolset paper 25

Padding Matters - Exploring Function Detection in PE Files: ...

引用

proceedings of the Fifteenth acm conference on data and application security and privacy

作者： Raphael Springer Alexander Schmitz Artur Leinweber Tobias Urban Christian Dietrich Institute for Internet Security Westphalian University of Applied Sciences Gelsenkirchen Germany

ISBN: (纸本)9798400714764

Function detection is a well-known problem in binary analysis. While prior work has focused on Linux/ELF, Windows/PE binaries have only partially been considered. This paper introduces FuncPEval, a dataset for Windows x86 and x64 PE files, featuring Chromium and the Conti ransomware, along with ground truth data for 1,092,820 function starts. Utilizing FuncPEval, we evaluate five heuristics-based (Ghidra, IDA, Nucleus, ***, SMDA) and three machine-learning-based (DeepDi, RNN, XDA) function start detection tools. Among these, IDA achieves the highest F1-score (98.44%) for Chromium x64, while DeepDi closely follows (97%) but stands out as the fastest. Towards explainability, we examine the impact of padding between functions on the detection results, finding all tested tools, except ***, are susceptible to randomized padding. The randomized padding significantly diminishes the effectiveness of the RNN, XDA, and Nucleus. Among the learning-based tools, DeepDi exhibits the least sensitivity, while Nucleus is the most adversely affected among the non-learning-based tools.

关键词： binary code analysis

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GuardiaNN: Fast and Secure On-Device Inference in TrustZone Using Embedded SRAM and Cryptographic Hardware 22

GuardiaNN: Fast and Secure On-Device Inference in TrustZone ...

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23rd acm/IFIP International Middleware conference (Middleware)

作者： Choi, Jinwoo Kim, Jaeyeon Lim, Chaemin Lee, Suhyun Lee, Jinho Song, Dokyung Kim, Youngsok Yonsei Univ Seoul South Korea

ISBN: (纸本)9781450393409

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent studies execute part of a DNN in a trusted execution environment (e.g., TrustZone) to isolate DNN execution from the other processes;however, as the trusted execution environments for mobile/embedded devices provide limited memory protection, DNN data remain unencrypted in DRAM and become vulnerable to physical attacks. The devices can prevent the physical attacks by keeping DNN data encrypted in DRAM;when DNN data get referenced during DNN execution, they get loaded to the SRAM and get decrypted by a CPU core. Unfortunately, using the SRAM with demand paging greatly increasesDNN execution time due to the inefficient use of the SRAM and the high CPU consumption of data encryption/decryption. In this paper, we present GuardiaNN, a fast and secure DNN framework which greatly accelerates DNN execution without sacrificing security guarantees. To accelerate secure DNN execution, GuardiaNN first reduces slow DRAM accesses with direct convolutions and maximizes the reuse of SRAM-stored data with DNNfriendly SRAM management. Then, aimed at dedicating the limited CPU resources to DNN execution, GuardiaNN offloads DNN data encryption/decryption onto secure cryptographic hardware and employs pipelining to overlap DNN execution with the encryption/decryption. For eight DNNs chosen from five representative mobile/embedded application domains, our implementation of GuardiaNN on STM32MP157C-DK2 development board achieves a geomean speedup of 15.3x and a geomean energy efficiency improvement of 15.2x over a baseline secure DNN framework which employs demand-paged SRAM to secure sensitive data.

关键词： on-device inference security ARM TrustZone DRAM encryption embedded SRAM cryptographic hardware

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CCIOT 2022 - 2022 7th International conference on Cloud Computing and Internet of Things

CCIOT 2022 - 2022 7th International Conference on Cloud Comp...

引用

7th International conference on Cloud Computing and Internet of Things, CCIOT 2022

ISBN: (纸本)9781450396738

The proceedings contain 11 papers. The topics discussed include: fast multi-label tumor classification based on homomorphic encryption;SecMesh: an efficient information security method for stream processing in edge-fog-cloud;a network-elastic scalable blockchain for privacy-preserving federated learning in cloud-edge collaboration industrial Internet of Things;SSL VPN over TCP and UDP tunnels: performance evaluation with different server-side congestion control;research on improved DV-hop algorithm based on multiple communication radius and hop distance correction;decentralized smart city of things: a blockchain tokenization-enabled architecture for digitization and authentication of assets in smart cities;software development and design of a cybersecurity system based on big data analysis technology;and research on blended teaching mode under the background of ‘Internet Plus’: application based on chaoxing learning pass platform.

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SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform 21

SteemOps: Extracting and Analyzing Key Operations in Steemit...

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11th acm conference on data and application security and privacy (CODASPY)

作者： Li, Chao Palanisamy, Balaji Xu, Runhua Xu, Jinlai Wang, Jingzhe Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA USA IBM Almaden Res Ctr San Jose CA USA

ISBN: (纸本)9781450381437

Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.

关键词： blockchain social network Steem dataset

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An Intelligent Doorbell Design Using Federated Deep Learning 21

An Intelligent Doorbell Design Using Federated Deep Learning

引用

3rd acm India Joint International conference on data Science and Management of data (CODS-COMAD)

作者： Patel, Vatsal Kanani, Sarth Pathak, Tapan Patel, Pankesh Ali, Muhammad Intizar Breslin, John Pandit Deendayal Petr Univ Gandhinagar Gujarat India NUI Galway Data Sci Inst Confirm SFI Res Ctr Smart Mfg Galway Ireland

ISBN: (纸本)9781450388177

Smart doorbells have been playing an important role in protecting our modern homes. Existing approaches of sending video streams to a centralized server (or Cloud) for video analytics have been facing many challenges such as latency, bandwidth cost and more importantly users' privacy concerns. To address these challenges, this paper showcases the ability of an intelligent smart doorbell based on Federated Deep Learning, which can deploy and manage video analytics applications such as a smart doorbell across Edge and Cloud resources. This platform can scale, work with multiple devices, seamlessly manage online orchestration of the application components. The proposed framework is implemented using state-of-the-art technology. We implement the Federated Server using the Flask framework, containerized using Nginx and Gunicorn, which is deployed on AWS EC2 and AWS Serverless architecture.

关键词： Federated Learning Internet of Things Video Analytics Artificial Intelligence Deep Learning Machine Learning privacy security

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OAuth 2.0-based authentication solution for FPGA-enabled cloud computing 14

OAuth 2.0-based authentication solution for FPGA-enabled clo...

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IEEE/acm 14th International conference on Utility and Cloud Computing

作者： Ince, Semih Espes, David Gogniat, Guy Lallet, Julien Santoro, Renaud Nokia Bell Labs Murray Hill NJ 07974 USA Univ Bretagne Occidentale Brest France Univ Bretagne Sud Lorient France

ISBN: (纸本)9781450391634

FPGA-enabled cloud computing is getting more and more common as cloud providers offer hardware accelerated solutions. In this context, clients need confidential remote computing. However Intellectual Properties and data are being used and communicated. So current security models require the client to trust the cloud provider blindly by disclosing sensitive information. In addition, the lack of strong authentication and access control mechanisms, for both the client and the provided FPGA in current solutions, is a major security drawback. To enhance security measures and privacy between the client, the cloud provider and the FPGA, an additional entity needs to be introduced: the trusted authority. Its role is to authenticate the client-FPGA pair and isolate them from the cloud provider. With our novel OAuth 2.0-based access delegation solution for FPGA-accelerated clouds, a remote confidential FPGA environment with a token-based access can be created for the client. Our solution allows to manage and securely allocate heterogeneous resource pools with enhanced privacy & confidentiality for the client. Our formal analysis shows that our protocol adds a very small latency which is suitable for real-time application.

关键词： Cloud FPGA security authentication OAuth

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Session details: Session 7: Encryption and privacy 22

Session details: Session 7: Encryption and Privacy

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proceedings of the Twelfth acm conference on data and application security and privacy

作者： Susanne Wetzel Stevens Institute of Technology

No abstract available.

ISBN: (纸本)9781450392204

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Session details: Session 2: privacy 22

Session details: Session 2: Privacy

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proceedings of the Twelfth acm conference on data and application security and privacy

作者： Maribel Fernandez King's College London

No abstract available.

ISBN: (纸本)9781450392204

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