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Secure Pull Printing with QR Codes and National eID Cards: A Software-oriented Design and an Open-source Implementation 21

Secure Pull Printing with QR Codes and National eID Cards: A...

引用

11th acm conference on data and application security and privacy, CODASPY 2021

作者： Leonelli, Matteo Morelli, Umberto Sciarretta, Giada Ranise, Silvio Fondazione Bruno Kessler Trento Italy Fondazione Bruno Kessler University of Trento Trento Italy

ISBN: (纸本)9781450381437

With more systems becoming digitised, enterprises are adopting cloud technologies and outsourcing non-critical services to reduce the pressure on IT departments. In this process, it is crucial to achieving the right balance between costs, usability and security;prioritising security over the rest when handling sensitive data. Considering the print management, often off-premise, many enterprises report at least one print-related security incident that led to data loss in the past year. This problem can damage the enterprise business, especially considering the fines prescribed by current regulations or its reputation. Focusing on securing enterprise printing, pull printing is the set of technologies and processes that allow the release of print jobs according to specific conditions;typically user authentication and proximity to a printer. We design a software-oriented pull printing infrastructure that supports a print release mechanism using QR codes and electronic IDentity cards as a second-factor authenticator. Our solution addresses the costs, as any medium-size organisation can adopt our open-source solution without additional devices or access badges;and the user experience, as we offer a driverless print environment and a user-friendly mobile application. © 2021 acm.

关键词： Sensitive data

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MLHat: Deployable Machine Learning for security Defense 21

MLHat: Deployable Machine Learning for Security Defense

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27th acm SIGKDD International conference on Knowledge Discovery and data Mining (KDD)

作者： Wang, Gang Ciptadi, Arridhana Ahmadzadeh, Ali UIUC Champaign IL 61820 USA TruEra Redmond WA USA Blue Hexagon San Francisco CA USA

ISBN: (纸本)9781450383325

The MLHat workshop aims to bring together academic researchers and industry practitioners to discuss the open challenges, potential solutions, and best practices to deploy machine learning at scale for security defense. The workshop will discuss related topics from both defender perspectives (white-hat) and the attacker perspectives ( black-hat). We call the workshop MLHats, to serve as a place for people who are interested in using machine learning to solve practical security problems. The workshop will focus on defining new machine learning paradigms under various security application contexts and identifying exciting new future research directions. At the same time, the workshop will also have a strong industry presence to provide insights into the challenges in deploying and maintaining machine learning models and the much-needed discussion on the capabilities that the state-of-the-arts failed to provide.

关键词： security and privacy Deployable Machine Learning Adversarial Machine Learning

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Enabling data Interoperability for Decentralized, Smart, and Connected Health applications 23

Enabling Data Interoperability for Decentralized, Smart, and...

引用

proceedings of the 8th acm/IEEE International conference on Connected Health: applications, Systems and Engineering Technologies

作者： Oshani Seneviratne Rensselaer Polytechnic Institute Troy NY USA

ISBN: (纸本)9798400701023

The digitization of healthcare is increasing, providing a wealth of data that can improve patient care and outcomes. However, different systems and formats often silo this data, presenting a significant challenge to achieving a connected and interoperable healthcare ecosystem. data interoperability is crucial for realizing the full potential of smart and connected health, which can improve patient outcomes and reduce costs. In this paper, we describe the key concepts and technologies enabling interoperability, including health information exchange, interoperability standards, and application programming interfaces enabling connected health applications built on blockchains. We also discuss the challenges and barriers to achieving data interoperability, such as privacy and security concerns, organizational and cultural barriers, and technical limitations. Furthermore, we highlight the benefits of data interoperability in healthcare, such as better patient outcomes, improved population health management, reduced costs, and increased efficiency. Finally, we discuss the potential future of data interoperability in healthcare, including the role of emerging privacy-preserving technologies.

关键词： interoperability

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Role-Based Deception in Enterprise Networks 21

Role-Based Deception in Enterprise Networks

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

作者： Anjum, Iffat Zhu, Mu Polinsky, Isaac Enck, William Reiter, Michael K. Singh, Munindar P. North Carolina State Univ Raleigh NC 27695 USA Duke Univ Durham NC USA

ISBN: (纸本)9781450381437

Historically, enterprise network reconnaissance is an active process, often involving port scanning. However, as routers and switches become more complex, they also become more susceptible to compromise. From this vantage point, an attacker can passively identify high-value hosts such as the workstations of IT administrators, C-suite executives, and. nance personnel. The goal of this paper is to develop a technique to deceive and dissuade such adversaries. We propose HoneyRoles, which uses honey connections to build metaphorical haystacks around the network traffic of client hosts belonging to high-value organizational roles. The honey connections also act as network canaries to signal network compromise, thereby dissuading the adversary from acting on information observed in network flows. We design a prototype implementation of HoneyRoles using an OpenFlow SDN controller and evaluate its security using the PRISM probabilistic model checker. Our performance evaluation shows that HoneyRoles has a small effect on network request completion time, and security analysis demonstrates that once an alert is raised, HoneyRoles can quickly identify the compromised switch with high probability. In doing so, we show that role-based network deception is a promising approach for defending against adversaries in compromised network devices.

关键词： deception software defined network cyber security data plane

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Assessing the Alignment of Social Robots with Trustworthy AI Design Guidelines: A Preliminary Research Study 21

Assessing the Alignment of Social Robots with Trustworthy AI...

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

作者： Chattopadhyay, Ankur Ali, Abdikadar Thaxton, Danielle Northern Kentucky Univ Comp Sci Dept Highland Hts KY 41099 USA

ISBN: (纸本)9781450381437

The last few years have seen a strong movement supporting the need of having intelligent consumer products align with specific design guidelines for trustworthy artificial intelligence (AI). This global movement has led to multiple institutional recommendations for ethically aligned trustworthy design of the AI driven technologies, like consumer robots and autonomous vehicles. There has been prior research towards finding security and privacy related vulnerabilities within various types of social robots. However, none of these previous works has studied the implications of these vulnerabilities in terms of the robot design aligning with trustworthy AI. In an attempt to address this gap in existing literature, we have performed a unique research study with two social robots - Zumi and Cozmo. In this study, we have explored flaws within the robot's system, and have analyzed these flaws to assess the overall alignment of the robot system design with the IEEE global standards on the design of ethically aligned trustworthy autonomous intelligent systems (IEEE A/IS Standards). Our initial research shows that the vulnerabilities and design weaknesses, which we found in these robots, can lead to hacking, injection attacks, and other malfunctions that might affect the technology users negatively. We test the intelligent functionalities in these robots to find faults, and conduct a preliminary examination of how these flaws can potentially result in non-adherence with the IEEE A/IS principles. Through this novel study, we demonstrate our approach towards determining alignment of social robots with benchmarks for trustworthy AI, thereby creating a case for prospective design improvements to address unique risks leading to issues with robot ethics and trust.

关键词： security and privacy Assessment Preliminary Research Study Alignment Social Robots Trustworthy AI Ethics IEEE A/IS Principles Standards Vulnerabilities Risks Design Flaws

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Formal Analysis of ReBAC Policy Mining Feasibility 21

Formal Analysis of ReBAC Policy Mining Feasibility

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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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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 ...

引用

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

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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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