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

共 556 条记录，以下是91-100 订阅

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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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PriveTAB : Secure and privacy-Preserving sharing of Tabular data 8

PriveTAB : Secure and Privacy-Preserving sharing of Tabular ...

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acm International Workshop on security and privacy Analytics (IWSPA) / 12th acm Annual conference on data and applications security and privacy (CODASPY)

作者： Kotal, Anantaa Piplai, Aritran Chukkapalli, Sai Sree Laya Joshi, Anupam Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9781450392303

Machine Learning has increased our ability to model large quantities of data efficiently in a short time. Machine learning approaches in many application domains require collecting large volumes of data from distributed sources and combining them. However, sharing of data from multiple sources leads to concerns about privacy. privacy regulations like European Union's General data Protection Regulation (GDPR) have specific requirements on when and how such data can be shared. Even when there are no specific regulations, organizations may have concerns about revealing their data. For example in cybersecurity, organizations are reluctant to share their network-related data to permit machine learning-based intrusion detectors to be built. This has, in particular, hampered academic research. We need an approach to make confidential data widely available for accurate data analysis without violating the privacy of the data subjects. privacy in shared data has been discussed in prior work focusing on anonymization and encryption of data. An alternate approach to make data available for analysis without sharing sensitive information is by replacing sensitive information with synthetic data that behave as original data for all analytical purposes. Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic samples that can have the same distributional characteristics as the original data. However, modeling tabular data using GAN is a non-trivial task. Tabular data contain a mix of categorical and continuous variables and require specialized constraints as described in the CTGAN model. In this paper, we propose a framework to generate privacypreserving synthetic data suitable for release for analytical purposes. The data is generated using the CTGAN approach, and so is analytically similar to the original dataset. To ensure that the generated data meet the privacy requirements, we use the principle of t-closeness. We ensure that the distributi

关键词： tabular datasets network datasets data privacy secure data sharing data generation generative adversarial networks machine learning

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GPU-based Private Information Retrieval for On-Device Machine Learning Inference 24

GPU-based Private Information Retrieval for On-Device Machin...

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29th acm International conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Lam, Maximilian Johnson, Jeff Xiong, Wenjie Maeng, Kiwan Gupta, Udit Li, Yang Lai, Liangzhen Leontiadis, Ilias Rhu, Minsoo Lee, Hsien-Hsin S. Reddi, Vijay Janapa Wei, Gu-Yeon Brooks, David Suh, G. Edward Meta AI Menlo Pk CA 94025 USA Harvard Univ Cambridge MA 02138 USA Virginia Tech Blacksburg VA USA Penn State University Pk PA USA Intel Santa Clara CA USA Cornell Univ Ithaca NY USA

ISBN: (纸本)9798400703720

On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To over-come this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than 20x over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over 5x additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to 100, 000 queries per second a > 100x throughput improvement over a CPU-based baseline-while maintaining model accuracy.

关键词： privacy security cryptography machine learning GPU performance

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Building a Commit-level dataset of Real-world Vulnerabilities 22

Building a Commit-level Dataset of Real-world Vulnerabilitie...

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

作者： Challande, Alexis David, Robin Renault, Guenael Quarkslab Paris France Inria Palaiseau France Inst Polytech Paris Palaiseau France ANSSI Paris France

ISBN: (纸本)9781450392204

While Common Vulnerabilities and Exposures (CVE) have become a de facto standard for publishing advisories on vulnerabilities, the state of current CVE databases is lackluster. Yet, CVE advisories are insufficient to bridge the gap with the vulnerability artifacts in the impacted program. Therefore, the community is lacking a public real-world vulnerabilities dataset providing such association. In this paper, we present a method restoring this missing link by analyzing the vulnerabilities from the Android Open Source Project (AOSP), an aggregate of more than 1,800 projects. It is the perfect target for building a representative dataset of vulnerabilities, as it covers the full spectrum that may be encountered in a modern system where a variety of low-level and higher-level components interact. More specifically, our main contribution is a dataset of more than 1,900 vulnerabilities, associating generic metadata (e.g. vulnerability type, impact level) with their respective patches at the commit granularity (e.g. fix commit-id, affected files, source code language). Finally, we also augment this dataset by providing precompiled binaries for a subset of the vulnerabilities. These binaries open various data usage, both for binary only analysis and at the interface between source and binary. In addition of providing a common baseline benchmark, our dataset release supports the community for data-driven software security research.

关键词： security vulnerabilities dataset vulnerability research patch detection binary matching

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Evaluating Website data Leaks through Spam Collection on Honeypots 25

Evaluating Website Data Leaks through Spam Collection on Hon...

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

作者： Oghenerukevwe Elohor Oyinloye Carol Fung Concordia Institute for Information Systems and Engineering Concordia University Montreal Quebec Canada

ISBN: (纸本)9798400714764

Nowadays, people rely heavily on online services in their daily lives such as communication, education, shopping, and entertainment. While online services offer convenience in daily living, users often receive a large number of spams as a result. While previous studies have linked spam receipt primarily to user behavior, this research proposes that spam can serve as a forensic indicator of data leaks by websites. To test our hypothesis, we conducted an experiment to deploy 148 honeypots across 370 websites spanning 12 communities. We monitored and audited the spams received by our honeypots for 47 weeks and analyzed their nature, pattern and origin. The results reveal that some legitimate websites leak user data despite having privacy policy statements. The findings also highlight that some websites automatically enroll users in newsletters or mailing lists without asking consent during the sign-up. This issue arises from conflating privacy policies with spam subscription and third party share agreements. To address these issues we suggest that regulators enforce websites to separate subscription agreement from privacy policy statements, and direct consent for third party share be requested at sign up. Also, websites should evaluate third party chain to ensure user data protection.

关键词： data privacy

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Efficient Dynamic Searchable Encryption with Forward privacy under the Decent Leakage 22

Efficient Dynamic Searchable Encryption with Forward Privacy...

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

作者： Watanabe, Yohei Ohara, Kazuma Iwamoto, Mitsugu Ohta, Kazuo Univ Electrocommun Chofu Tokyo Japan NICT Tokyo Japan AIST Tokyo Japan

ISBN: (纸本)9781450392204

Dynamic searchable symmetric encryption (SSE) enables clients to update and search encrypted data stored on a server and provides efficient search operations instead of leakages of inconsequential information. The amount of permitted leakage is a crucial factor of dynamic SSE;more leakage allows us to design an efficient scheme, while leakage attacks tell us that the leakage has a real-world impact. Leakage-abuse attacks (NDSS 2012) and subsequent works suggest that dynamic SSE schemes should not unnecessarily reveal extra information during the search procedure, and in particular, file-injection attacks (USENIX security 2016) showed that forward privacy, which restricts the leakage during the addition procedure, is a vital security notion for dynamic SSE. In this paper, we propose a new dynamic SSE scheme with a good balance of efficiency and security levels;our scheme achieves both high efficiency and forward-privacy and only requires the decent leakage, i.e., only allows the leakage of search and access patterns during search operations. Specifically, we first show there is still no such scheme by uncovering a flaw in the security proof of Etemad et al.'s scheme (PoPETs 2018) and showing that extra leakage is required to fix it. We then propose the first forward-private dynamic SSE scheme that only requires symmetric-key primitives and the standard, decent leakage to prove the security. Although the client's information is slightly larger than existing schemes, our experimental results show that our scheme is comparable to Etemad et al.'s scheme, which is the most-efficient-ever scheme with forward privacy, in terms of efficiency.

关键词： dynamic searchable encryption forward privacy encrypted database

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Leveraging Disentangled Representations to Improve Vision-Based Keystroke Inference Attacks Under Low data Constraints 22

Leveraging Disentangled Representations to Improve Vision-Ba...

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

作者： Lim, John Frahm, Jan-Michael Monrose, Fabian Univ N Carolina Chapel Hill NC 27599 USA

ISBN: (纸本)9781450392204

Keystroke inference attacks are a form of side-channel attacks in which an attacker leverages various techniques to recover a user's keystrokes as she inputs information into some display (e.g., while sending a text message or entering her pin). Typically, these attacks leverage machine learning approaches, but assessing the realism of the threat space has lagged behind the pace of machine learning advancements, due in-part, to the challenges in curating large real-life datasets. We aim to overcome the challenge of having limited number of real data by introducing a video domain adaptation technique that is able to leverage synthetic data through supervised disentangled learning. Specifically, for a given domain, we decompose the observed data into two factors of variation: Style and Content. Doing so provides four learned representations: real-life style, synthetic style, real-life content and synthetic content. Then, we combine them into feature representations from all combinations of style-content pairings across domains, and train a model on these combined representations to classify the content (i.e., labels) of a given datapoint in the style of another domain. We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover. We show that our method prevents our model from overfitting to a small real-life training set, indicating that our method is an effective form of data augmentation, thereby making keystroke inference attacks more practical.

关键词： Gaze detection data leak detection and prevention security and privacy novel datasets

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