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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 1522 条记录，以下是681-690 订阅

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Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization 20

Renyi Differentially Private ADMM for Non-Smooth Regularized...

引用

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

作者： Chen, Chen Lee, Jaewoo Univ Georgia Athens GA 30602 USA

ISBN: (纸本)9781450371070

In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as L-1 norm or nuclear norm, under Renyi differential privacy (RDP). To solve the problem, we propose two stochastic alternating direction method of multipliers (ADMM) algorithms: ssADMM based on gradient perturbation and mpADMM based on output perturbation. Both algorithms decompose the original problem into sub-problems that have closed-form solutions. the first algorithm, ssADMM, applies the recent privacy amplification result for RDP to reduce the amount of noise to add. the second algorithm, mpADMM, numerically computes the sensitivity of ADMM variable updates and releases the updated parameter vector at the end of each epoch. We compare the performance of our algorithms with several baseline algorithms on both real and simulated datasets. Experimental results show that, in high privacy regimes (small.), ssADMM and mpADMM outperform baseline algorithms in terms of classification and feature selection performance, respectively.

关键词： differential privacy convex optimization ADMM regularization

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A Framework for data Sharing between Healthcare Providers using Blockchain 5

A Framework for Data Sharing between Healthcare Providers us...

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5th International conference on Internet of things, Big data and security (IoTBDS)

作者： Alzahrani, Ahmed G. Alenezi, Ahmed Atlam, Hany F. Wills, Gary Univ Southampton Elect & Comp Sci Dept Univ Rd Southampton SO17 1BJ Hants England King Abdulaziz Univ Dept Comp Sci Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia Northern Border Univ Comp Sci Dept Fac Comp & Informat Technol Ar Ar Saudi Arabia

ISBN: (纸本)9789897584268

the healthcare data are considered as a highly valuable source of information that can improve healthcare systems to be more intelligent and improve the quality of the provided services. However, due to security and privacy issues, sharing data between healthcare organisations is challenging. this has led to data shortage in the healthcare sector which is considered as a significant issue not only in the Kingdom of Saudi Arabia (KSA) but also worldwide. the primary objective of conducting this paper is to investigate the various factors that enable secure sharing and exchange of healthcare information between different healthcare providers in the KSA. It starts by discussing the current literature and frameworks for managing healthcare data information and the challenges that health providers encounter, particularly when it comes to issues such as data security, patient privacy, and healthcare information exchange. these challenges in managing healthcare data have necessitated the need for implementing a solution that can allow medical providers to have access to updated healthcare information. Attention in the healthcare sector has been drawn to blockchain technology as a part of the solution, especially after the technology was successfully applied in the financial sector to improve the security of financial transactions, particularly involving digital currencies such as Bitcoin. therefore, a framework based on the blockchain technology has been proposed to achieve the goals of the present research.

关键词： Blockchain Healthcare Systems Sharing data privacy security Kingdom of Saudi Arabia

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A Review on Biometric based Secure data Transmission 5

A Review on Biometric based Secure Data Transmission

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5th International conference on Inventive Computation Technologies (ICICT)

作者： Kumbhare, Mithun G. Padole, Palash S. Shende, Diksha P. Meshram, Sanchana S. Baraskar, Shailesh S. Raut, Swati B. JD Coll Engn & Management Dept Comp Sci & Engn Nagpur Maharashtra India

ISBN: (纸本)9781728146850

Cloud technology plays a vital role in the process of business advancement, where it realizes advanced research outcomes in a brief span. However, in case of cloud computing, the data is mostly outsourced. there always remains a concern for the security and integrity of data. In addition, user privacy remains as a vital area of concern. Many systems and techniques have developed to address these issues, but still there is always a scope for improvement. While tending to the issues identified with the client protection, information security and trustworthiness, we should think about the productivity of the framework while getting to and scanning for the information. In this paper, we discuss about the major challenges in cloud environment. Additionally, displayed is a concise review on existing examination in the previously mentioned areas.

关键词： Secure Transmission Public Key Infrastructure privacy Preserving in cloud Biometric in Cloud

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Cost Effective IoT-based Remote Healthcare Monitoring System for Developing Countries 5

Cost Effective IoT-based Remote Healthcare Monitoring System...

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5th IEEE International conference on Smart Internet of things, SmartIoT 2021

作者： Yaakob, Naimah Almashor, Mahathir Muhammed Ahmed, Ahmed Fathi Faculty of Electronic Engineering Technology University Malaysia Perlis Arau Perlis Pauh Putra 02600 Malaysia CSIRO Data61 and Cyber Security Cooperative Research Centre Corner Vimiera and Pembroke Roads MarsfieldNSW2122 Australia

ISBN: (纸本)9781665445115

there is a critical need for the remote monitoring of patients, especially given the current pandemic situation. this is ever more acute in developing nations, where access to healthcare is reliant on already stretched medical services. Despite this, the traditional mode of operation remains Face-to-Face (F2F), even for routine follow-up examinations. the work herein details an Internet-of-things (IoT) based prototype that allows remote monitoring of patients in a cost-effective way. the prototype was designed to be employed in environments where resources are limited. It is particularly useful in the monitoring of patients that require constant surveillance but not necessarily immediate attention, such as the elderly or those recovering from medical procedures. A combination of sensors (including an LM35 for body temperatures) is connected to an Arduino UNO main controller board. Sensed data is processed both on-site and sent to a cloud-based system comprising of both a database and a web-application. Alerts are sent at the local and cloud levels should readings indicate potential health issues, with medical professionals able to access patient data easily. In this way, patients can be safely monitored remotely without the need for physically present caregivers, and thus also decrease the burden on a nation’s overly strained medical personnel. © 2021 IEEE.

关键词： Internet of things

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OAuth 2.0-based authentication solution for FPGA-enabled cloud computing 21

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

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proceedings of the 14th IEEE/acm International conference on Utility and Cloud Computing Companion

作者： Semih Ince David Espes Guy Gogniat Julien Lallet Renaud Santoro Nokia Bell Labs Univ. Bretagne Occidentale Univ. Bretagne Sud

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 OAuth security authentication FPGA

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GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models 20

GAN-Leaks: A Taxonomy of Membership Inference Attacks agains...

引用

acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Chen, Dingfan Yu, Ning Zhang, Yang Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Univ Maryland College Pk MD 20742 USA Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781450370899

Deep learning has achieved overwhelming success, spanning from discriminative models to generative models. In particular, deep generative models have facilitated a new level of performance in a myriad of areas, ranging from media manipulation to sanitized dataset generation. Despite the great success, the potential risks of privacy breach caused by generative models have not been analyzed systematically. In this paper, we focus on membership inference attack against deep generative models that reveals information about the training data used for victim models. Specifically, we present the first taxonomy of membership inference attacks, encompassing not only existing attacks but also our novel ones. In addition, we propose the first generic attack model that can be instantiated in a large range of settings and is applicable to various kinds of deep generative models. Moreover, we provide a theoretically grounded attack calibration technique, which consistently boosts the attack performance in all cases, across different attack settings, data modalities, and training configurations. We complement the systematic analysis of attack performance by a comprehensive experimental study, that investigates the effectiveness of various attacks w.r.t. model type and training configurations, over three diverse application scenarios (i.e., images, medical data, and location data).

关键词： Membership inference attacks deep learning generative models privacy-preserving machine learning

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Characterizing Smart Home IoT Traffic in the Wild 5

Characterizing Smart Home IoT Traffic in the Wild

引用

5th IEEE/acm International conference on Internet-of-things Design and Implementation (acm/IEEE IoTDI)

作者： Mazhar, M. Hammad Shafiq, Zubair Univ Iowa Dept Comp Sci Iowa City IA 52242 USA

ISBN: (纸本)9781728166025

As the smart home IoT ecosystem flourishes, it is imperative to gain a better understanding of the unique challenges it poses in terms of management, security, and privacy. Prior studies are limited because they examine smart home IoT devices in testbed environments or at a small scale. To address this gap, we present a measurement study of smart home IoT devices in the wild by instrumenting home gateways and passively collecting real-world network traffic logs from more than 200 homes across a large metropolitan area in the United States. We characterize smart home IoT traffic in terms of its volume, temporal patterns, and external endpoints along with focusing on certain security and privacy concerns. We first show that traffic characteristics reflect the functionality of smart home IoT devices such as smart TVs generating high volume traffic to content streaming services following diurnal patterns associated with human activity. While the smart home IoT ecosystem seems fragmented, our analysis reveals that it is mostly centralized due to its reliance on a few popular cloud and DNS services. Our findings also highlight several interesting security and privacy concerns in smart home IoT ecosystem such as the need to improve policy-based access control for IoT traffic, lack of use of application layer encryption, and prevalence of third-party advertising and tracking services. Our findings have important implications for future research on improving management, security, and privacy of the smart home IoT ecosystem.

关键词： In the wild Measurement privacy security Smart home IoT Traffic analysis

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Attacking and Protecting Tunneled Traffic of Smart Home Devices 20

Attacking and Protecting Tunneled Traffic of Smart Home Devi...

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

作者： Alshehri, Ahmed Granley, Jacob Yue, Chuan Colroado Sch Mines Golden CO 80401 USA

ISBN: (纸本)9781450371070

the number of smart home IoT (Internet of things) devices has been growing fast in recent years. Along with the great benefits brought by smart home devices, new threats have appeared. One major threat to smart home users is the compromise of their privacy by traffic analysis (TA) attacks. Researchers have shown that TA attacks can be performed successfully on either plain or encrypted traffic to identify smart home devices and infer user activities. Tunneling traffic is a very strong countermeasure to existing TA attacks. However, in this work, we design a Signature based Tunneled Traffic Analysis (STTA) attack that can be effective even on tunneled traffic. Using a popular smart home traffic dataset, we demonstrate that our attack can achieve an 83% accuracy on identifying 14 smart home devices. We further design a simple defense mechanism based on adding uniform random noise to effectively protect against our TA attack without introducing too much overhead. We prove that our defense mechanism achieves approximate differential privacy.

关键词： Internet of things (IoT) Traffic Analysis (TA) privacy Differential privacy Smart Homes Attacks Defenses

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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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Understanding privacy Awareness in Android App Descriptions Using Deep Learning 20

Understanding Privacy Awareness in Android App Descriptions ...

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

作者： Feichtner, Johannes Gruber, Stefan Graz Univ Technol Graz Austria Secure Informat Technol Ctr Austria A SIT Graz Austria

ISBN: (纸本)9781450371070

Permissions are a key factor in Android to protect users' privacy. As it is often not obvious why applications require certain permissions, developer-provided descriptions in Google Play and third-party markets should explain to users how sensitive data is processed. Reliably recognizing whether app descriptions cover permission usage is challenging due to the lack of enforced quality standards and a variety of ways developers can express privacy-related facts. We introduce a machine learning-based approach to identify critical discrepancies between developer-described app behavior and permission usage. By combining state-of-the-art techniques in natural language processing (NLP) and deep learning, we design a convolutional neural network (CNN) for text classification that captures the relevance of words and phrases in app descriptions in relation to the usage of dangerous permissions. Our system predicts the likelihood that an app requires certain permissions and can warn about descriptions in which the requested access to sensitive user data and system features is textually not represented. We evaluate our solution on 77,000 real-world app descriptions and find that we can identify individual groups of dangerous permissions with a precision between 71% and 93%. To highlight the impact of individual words and phrases, we employ a model explanation algorithm and demonstrate that our technique can successfully bridge the semantic gap between described app functionality and its access to security- and privacy-sensitive resources.

关键词： Android Machine Learning Description Permission NLP CNN

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