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Prediction of Mobile App privacy Preferences with User Profiles via Federated Learning 22

Prediction of Mobile App Privacy Preferences with User Profi...

引用

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

作者： Brandao, Andre Mendes, Ricardo Vilela, Joao P. Univ Porto Fac Sci CRACS INESCTEC CISUC Porto Portugal Univ Porto Fac Sci Dept Comp Sci Porto Portugal Univ Coimbra CISUC Dept Informat Engn Coimbra Portugal

ISBN: (纸本)9781450392204

Permission managers in mobile devices allow users to control permissions requests, by granting of denying application's access to data and sensors. However, existing managers are ineffective at both protecting and warning users of the privacy risks of their permissions' decisions. Recent research proposes privacy protection mechanisms through user profiles to automate privacy decisions, taking personal privacy preferences into consideration. While promising, these proposals usually resort to a centralized server towards training the automation model, thus requiring users to trust this central entity. In this paper we propose a methodology to build privacy profiles and train neural networks for prediction of privacy decisions, while guaranteeing user privacy, even against a centralized server. Specifically, we resort to privacy-preserving clustering techniques towards building the privacy profiles, that is, the server computes the centroids (profiles) without access to the underlying data. Then, using federated learning, the model to predict permission decisions is learnt in a distributed fashion while all data remains locally in the users' devices. Experiments following our methodology show the feasibility of building a personalized and automated permission manager guaranteeing user privacy, while also reaching a performance comparable to the centralized state of the art, with an F1-score of 0.9.

关键词： privacy mobile devices automated prediction federated learning

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Is there a security Mindset and Can it be Taught? 21

Is there a Security Mindset and Can it be Taught?

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

作者： Siraj, Ambareen Sridhar, Nigamanth Hamilton, John A. Drew, Jr. Khan, Latifur Kaza, Siddharth Gupta, Maanak Mittal, Sudip Tennessee Technol Univ Cookeville TN 38505 USA Cleveland State Univ Cleveland OH 44115 USA Mississippi State Univ Mississippi State MS 39762 USA Univ Texas Dallas Richardson TX 75083 USA Towson Univ Towson MD USA Univ North Carolina Wilmington Wilmington NC USA

The field of cybersecurity is becoming very dynamic, and needs continuous evolution. This requires not only the formal and informal education, but a security mindset to be developed for our future workforce. This pane... 详细信息

ISBN: (纸本)9781450381437

关键词： Cybersecurity

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privacy-Preserving Maximum Matching on General Graphs and its application to Enable privacy-Preserving Kidney Exchange 22

Privacy-Preserving Maximum Matching on General Graphs and it...

引用

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

作者： Breuer, Malte Meyer, Ulrike Wetzel, Susanne Rhein Westfal TH Aachen Aachen Germany Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781450392204

To this day, there are still some countries where the exchange of kidneys between multiple incompatible patient-donor pairs is restricted by law. Typically, legal regulations in this context are put in place to prohibit coercion and manipulation in order to prevent a market for organ trade. Yet, in countries where kidney exchange is practiced, existing platforms to facilitate such exchanges generally lack sufficient privacy mechanisms. In this paper, we propose a privacy-preserving protocol for kidney exchange that not only addresses the privacy problem of existing platforms but also is geared to lead the way in overcoming legal issues in those countries where kidney exchange is still not practiced. In our approach, we use the concept of secret sharing to distribute the medical data of patients and donors among a set of computing peers in a privacy-preserving fashion. These computing peers then execute our new Secure Multi-Party Computation (SMPC) protocol among each other to determine an optimal set of kidney exchanges. As part of our new protocol, we devise a privacy-preserving solution to the maximum matching problem on general graphs. We have implemented the protocol in the SMPC benchmarking framework MP-SPDZ and provide a comprehensive performance evaluation. Furthermore, we analyze the practicality of our protocol when used in a dynamic setting where patients and donors arrive and depart over time) based on a data set from the United Network for Organ Sharing.

关键词： Kidney Exchange privacy Secure Multi-Party Computation Matching Algorithms

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ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach 24

ERASER: Machine Unlearning in MLaaS via an Inference Serving...

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31st conference on Computer and Communications security

作者： Hu, Yuke Lou, Jian Liu, Jiaqi Ni, Wangze Lin, Feng Qin, Zhan Ren, Kui Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798400706363

Over the past years, Machine Learning-as-a-Service (MLaaS) has received a surging demand for supporting Machine Learning-driven services to offer revolutionized user experience across diverse application areas. MLaaS provides inference service with low inference latency based on an ML model trained using a dataset collected from numerous individual data owners. Recently, for the sake of data owners' privacy and to comply with the "right to be forgotten (RTBF)" as enacted by data protection legislation, many machine unlearning methods have been proposed to remove data owners' data from trained models upon their unlearning requests. However, despite their promising efficiency, almost all existing machine unlearning methods handle unlearning requests independently from inference requests, which unfortunately introduces a new security issue of inference service obsolescence and a privacy vulnerability of undesirable exposure for machine unlearning in MLaaS. In this paper, we propose the ERASER framework for machinE unleaRning in MLaAS via an inferencE seRving-aware approach. ERASER strategically chooses appropriate unlearning execution timing to address the inference service obsolescence issue. A novel inference consistency certification mechanism is proposed to avoid the violation of RTBF principle caused by postponed unlearning executions, thereby mitigating the undesirable exposure vulnerability. ERASER offers three groups of design choices to allow for tailor-made variants that best suit the specific environments and preferences of various MLaaS systems. Extensive empirical evaluations across various settings confirm ERASER's effectiveness, e.g., it can effectively save up to 99% of inference latency and 31% of computation overhead over the inference-oblivion baseline.

关键词： Machine Unlearning Machine Learning as a Service

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An Introduction to Federated Computation 22

An Introduction to Federated Computation

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International conference on Management of data (SIGMOD)

作者： Bharadwaj, Akash Cormode, Graham Meta AI Menlo Pk CA 94025 USA

ISBN: (纸本)9781450392495

Federated Computation is an emerging area that seeks to provide stronger privacy for user data, by performing large scale, distributed computations where the data remains in the hands of users. Only the necessary summary information is shared, and additional security and privacy tools can be employed to provide strong guarantees of secrecy. The most prominent application of federated computation is in training machine learning models (federated learning), but many additional applications are emerging, more broadly relevant to data management and querying data. This tutorial gives an overview of federated computation models and algorithms. It includes an introduction to security and privacy techniques and guarantees, and shows how they can be applied to solve a variety of distributed computations providing statistics and insights to distributed data. It also discusses the issues that arise when implementing systems to support federated computation, and open problems for future research.

关键词： federated computation federated analytics federated learning distributed computation privacy

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DP-UTIL: Comprehensive Utility Analysis of Differential privacy in Machine Learning 22

DP-UTIL: Comprehensive Utility Analysis of Differential Priv...

引用

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

作者： Jarin, Ismat Eshete, Birhanu Univ Michigan Dearborn MI 48128 USA

ISBN: (纸本)9781450392204

Differential privacy (DP) has emerged as a rigorous formalism to quantify privacy protection provided by an algorithm that operates on privacy sensitive data. In machine learning (ML), DP has been employed to limit inference/disclosure of training examples. Prior work leveraged DP across the ML pipeline, albeit in isolation, often focusing on mechanisms such as gradient perturbation. In this paper, we present DP-UTIL, a holistic utility analysis framework of DP across the ML pipeline with focus on input perturbation, objective perturbation, gradient perturbation, output perturbation, and prediction perturbation. Given an ML task on privacy-sensitive data, DP-UTIL enables a ML privacy practitioner to perform holistic comparative analysis on the impact of DP in these five perturbation spots, measured in terms of model utility loss, privacy leakage, and the number of truly revealed training samples. We evaluate DP-UTIL over classification tasks on vision, medical, and financial datasets, using two representative learning algorithms (logistic regression and deep neural network) against membership inference attack as a case study attack. One of the highlights of our results is that prediction perturbation consistently achieves the lowest utility loss on all models across all datasets. In logistic regression models, objective perturbation results in lowest privacy leakage compared to other perturbation techniques. For deep neural networks, gradient perturbation results in lowest privacy leakage. Moreover, our results on true revealed records suggest that as privacy leakage increases a differentially private model reveals a greater number of member samples. Overall, our findings suggest that to make informed decisions as to the choice of perturbation mechanisms, a ML privacy practitioner needs to examine the dynamics among optimization techniques (convex vs. non-convex), number of classes, and privacy budget.

关键词： Differential privacy Machine Learning Membership Inference Attack

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An Investigative Study on the privacy Implications of Mobile E-scooter Rental Apps 22

An Investigative Study on the Privacy Implications of Mobile...

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15th acm conference on security and privacy in Wireless and Mobile Networks (acm WiSec)

作者： Vinayaga-Sureshkanth, Nisha Wijewickrama, Raveen Maiti, Anindya Jadliwala, Murtuza Univ Texas San Antonio San Antonio TX 78249 USA Univ Oklahoma Norman OK 73019 USA

ISBN: (纸本)9781450392167

E-scooter rental services have significantly expanded the micromobility paradigm of short-distance urban and suburban transportation since their inception in 2017. Service providers around the world have followed a common rental model wherein customers (i.e., riders or users) download and install a mobile application for locating (finding) and renting e-scooters. Unlike many other app categories, e-scooter rental apps require a set of privacy-sensitive user data as a functional requirement. Unfortunately, privacy-related questions such as how much user data is being collected by these apps?, is user data being safely handled once acquired?, and with whom the collected user data is being shared? are not readily known to customers. Answering such questions can be critical for users in determining which e-scooter rental services are sufficiently trustworthy per their personal privacy preferences. In this paper, we conduct a comprehensive analysis of e-scooter rental apps to answer these and other research questions related to user data collection, third-party involvement, usefulness of privacy policies, and evolution of user data management by different e-scooter apps/services over time. Our findings will create awareness among consumers vis-a-vis the data they share with service providers in return for the received e-scooter rental service, and it can also evoke more accountability and transparency from service providers towards their efforts and processes on protecting consumer privacy.

关键词： E-scooter Mobile Apps application Analysis privacy Policies

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ProSPEC: Proactive security Policy Enforcement for Containers 22

ProSPEC: Proactive Security Policy Enforcement for Container...

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

作者： Kermabon-Bobinnec, Hugo Gholipourchoubeh, Mahmood Bagheri, Sima Majumdar, Suryadipta Jarraya, Yosr Pourzandi, Makan Wang, Lingyu Concordia Univ Montreal PQ Canada Ericsson Secur Res Montreal PQ Canada

ISBN: (纸本)9781450392204

By providing lightweight and portable support for cloud native applications, container environments have gained significant momentum lately. A container orchestrator such as Kubernetes can enable the automatic deployment and maintenance of a large number of containerized applications. However, due to its critical role, a container orchestrator also attracts a wide range of security threats exploiting misconfigurations or implementation flaws. Moreover, enforcing security policies at runtime against such security threats becomes far more challenging, as the large scale of container environments implies high complexity, while the high dynamicity demands a short response time. In this paper, we tackle this key security challenge to container environments through a proactive approach, namely, ProSPEC. Our approach leverages learning-based prediction to conduct the computationally intensive steps (e.g., security verification) in advance, while keeping the runtime steps (e.g., policy enforcement) lightweight. Consequently, ProSPEC can ensure a practical response time (e.g., less than 10 ms in contrast to 600 ms with one of the most popular existing approaches) for large container environments (up to 800 Pods).

关键词： Container security Policy enforcement Proactive security

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Deep Learning for Network Traffic data 22

Deep Learning for Network Traffic Data

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28th acm SIGKDD conference on Knowledge Discovery and data Mining (KKD)

作者： Marwah, Manish Arlitt, Martin Micro Focus Santa Clara CA 95054 USA Micro Focus Calgary AB Canada

ISBN: (纸本)9781450393850

Network traffic data is key in addressing several important cyber-security problems, such as intrusion and malware detection, and network management problems, such as application and device identification. However, it poses several challenges to building machine learning models. Two main challenges are manual feature engineering and scarcity of training data due to privacy and security concerns. In this tutorial we provide a comprehensive review of recent advances to address these challenges through use of deep learning. Network traffic data can be cast as a multivariate time-series (sequential) data, attributed graph data, or image data to leverage representation learning architectures available in deep learning. To preserve data privacy, generative methods, such as GANs and autoregressive neural architectures can be used to synthesize realistic network traffic data. In particular, our tutorial is organized into three parts: 1) we describe network traffic data, applications to security and network management, and challenges;2) we present different deep learning architectures used for representation learning instead of feature engineering of network traffic data;and, 3) we describe use of generative neural models for synthetic generation of network traffic data.

关键词： data science computer security synthetic data generation representation learning machine learning deep learning network traffic data

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Multi-Level Node Authorization using Recurrent Neural Networks for Secure Health Monitoring System

Multi-Level Node Authorization using Recurrent Neural Networ...

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2023 International conference on Sustainable Communication Networks and application, ICSCNA 2023

作者： Narayana, V. Lakshman Syamalatha, P. Vatsalya, P. Sricharitha, V. Akhila, V. Vignan's Nirula Institute of Technology and Science for Women Department of Cse Peda Palakaluru Guntur India

ISBN: (纸本)9798350313987

Rapid technological progress has allowed for the creation of novel healthcare solutions that aims to improve patient care and monitoring. data security and privacy are given high priority in the proposed system, which is a health monitoring platform that leverages the capabilities of the Internet of Things (IOT) and deep learning methods like Transformers in RNN to gather and analyse health data with high precision. For reliable anomaly detection, early risk prediction, and individualized healthcare recommendations, deep learning algorithms are used to sift through the data. The main objective of this research is to design an efficient model for strong authentication to improve the security levels. Given the delicate nature of individual health information, security and privacy are of the utmost importance in health monitoring systems. The SHMS addresses these issues by incorporating strong authentication and encryption technologies to protect information at rest and in transit. An individual's right to decide how their personal health data is used is also carefully protected by these systems. This study describes a major advancement in healthcare technology: a system for secure, accurate, and real-time health monitoring that uses the Internet of Things and a Transformer-based RNN in deep learning. This technology not only improves the ability of healthcare providers to provide timely and effective care, but also protects the privacy and security of patient data. This research presents a Multi-Level Node Authorization and Access Control Model with Dual Key Authentication using Recurrent Neural Networks (MLNA-acm-DKA-RNN) for secure health monitoring system. The proposed model when compared with the traditional models exhibit better performance. © 2023 IEEE.

关键词： Access Control Health Monitoring Internet of Things Key Authentication Node Authentication Recurrent Neural Networks security

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