2021 18th International Conference on Privacy, Security and Trust (PST)最新文献_第4页

Securing Critical Infrastructure Through Innovative Use Of Merged Hierarchical Deep Neural Networks 通过创新使用合并层次深度神经网络保护关键基础设施

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647771

Lav Gupta

{"title":"Securing Critical Infrastructure Through Innovative Use Of Merged Hierarchical Deep Neural Networks","authors":"Lav Gupta","doi":"10.1109/PST52912.2021.9647771","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647771","url":null,"abstract":"Multi-clouds are becoming central to large and modern applications including those in business, industry and critical infrastructure sectors. Designers usually deploy these clouds hierarchically to get the best advantage of low latency of the edge clouds and high processing capabilities of the core clouds. The data that flows into the clouds for processing and storage and moves out to other system domains for further use must cross multiple trust boundaries, and as a result, face large attack surfaces. This gives malicious actors abundant opportunity to penetrate and potentially harm organizations or bring down critical services causing widespread disruptions and mayhem. Deep neural network models can be used in innovative ways to protect the confidentiality and integrity of dataflows in the clouds. However, the use of deep learning comes with some challenges. In large multi-location and multi-cloud environments, deep learning models grow rapidly in size and complexity, inhibiting fast training of cloud models and making it difficult to maintain accuracy of detection of known and unknown attacks on the data-in-motion. This impedes their use in critical infrastructure services. We propose innovative distributed-hierarchical-merged models, which make use of cooperative training at the edge and the core clouds, and the power of data and model parallelisms, to achieve rapid training with high accuracy. Our broad objectives in this paper are twofold: Firstly, we show that merged hierarchical deep learning models, working cooperatively in the multi-cloud, significantly reduce the parameters to be trained and results in faster core cloud training time. Secondly, training the merged core model with distribute strategy for data parallelism on CPUs and GPUs further reduces the training time significantly. We also show that while achieving improvement by about 25% over unmerged models and the accuracy of detection of unknown attacks in the range 96.9-99.5%.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

DaRoute: Inferring trajectories from zero-permission smartphone sensors DaRoute:从零许可智能手机传感器推断轨迹

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647811

C. Roth, N. Dinh, Marc Roßberger, D. Kesdogan

{"title":"DaRoute: Inferring trajectories from zero-permission smartphone sensors","authors":"C. Roth, N. Dinh, Marc Roßberger, D. Kesdogan","doi":"10.1109/PST52912.2021.9647811","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647811","url":null,"abstract":"Nowadays, smartphones are equipped with a multitude of sensors, including GPS, that enables location-based services. However, leakage or misuse of user locations poses a severe privacy threat, motivating operating systems to usually restrict direct access to these resources for applications. Nevertheless, this work demonstrates how an adversary can deduce sensitive location information by inferring a vehicle’s trajectory through inbuilt motion sensors collectible by zero-permission mobile apps. Therefore, the presented attack incorporates data from the accelerometer, the gyroscope, and the magnetometer. We then extract so-called path events from raw data to eventually match them against reference data from OpenStreetMap. At the example of real-world data from three different cities, several drivers, and different smartphones, we show that our approach can infer traveled routes with high accuracy within minutes while robust to sensor errors. Our experiments show that even for areas as large as approximately 4500 $mathrm{k}mathrm{m}^{2}$, the accuracy of detecting the correct route is as high as 87.14%, significantly outperforming similar approaches from Narain et al. and Waltereit et al.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114644578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

FOX: Fooling with Explanations : Privacy Protection with Adversarial Reactions in Social Media 福克斯:玩弄解释:隐私保护与社交媒体中的敌对反应

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647778

Noreddine Belhadj Cheikh, Abdessamad Imine, M. Rusinowitch

{"title":"FOX: Fooling with Explanations : Privacy Protection with Adversarial Reactions in Social Media","authors":"Noreddine Belhadj Cheikh, Abdessamad Imine, M. Rusinowitch","doi":"10.1109/PST52912.2021.9647778","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647778","url":null,"abstract":"Socia1 media data has been mined over the years to predict individual sensitive attributes such as political and religious beliefs. Indeed, mining such data can improve the user experience with personalization and freemium services. Still, it can also be harmful and discriminative when used to make critical decisions, such as employment. In this work, we investigate social media privacy protection against attribute inference attacks using machine learning explainability and adversarial defense strategies. More precisely, we propose FOX (FOoling with eXplanations), an adversarial attack framework to explain and fool sensitive attribute inference models by generating effective adversarial reactions. We evaluate the performance of FOX with other SOTA baselines in a black-box setting by attacking five gender attribute classifiers trained on Facebook pictures reactions, specifically (i) comments generated by Facebook users excluding the picture owner, and (ii) textual tags (i.e., alttext) generated by Facebook. Our experiments show that FOX successfully fools (about 99.7% and 93.2% of the time) the classifiers, outperforms the SOTA baselines and gives a good transferability of adversarial features.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121736514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Searching on Non-Systematic Erasure Codes 非系统纠删码的检索

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647779

Atthapan Daramas, Vimal Kumar

引用次数: 0

Cross the Chasm: Scalable Privacy-Preserving Federated Learning against Poisoning Attack 跨越鸿沟:防止中毒攻击的可扩展隐私保护联邦学习

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647750

Yiran Li, Guiqiang Hu, Xiaoyuan Liu, Zuobin Ying

引用次数: 2

Towards Privacy-Preserving Classification-as-a-Service for DGA Detection 面向DGA检测的隐私保护分类即服务

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647755

Arthur Drichel, M. A. Gurabi, Tim Amelung, Ulrike Meyer

{"title":"Towards Privacy-Preserving Classification-as-a-Service for DGA Detection","authors":"Arthur Drichel, M. A. Gurabi, Tim Amelung, Ulrike Meyer","doi":"10.1109/PST52912.2021.9647755","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647755","url":null,"abstract":"Domain generation algorithm (DGA) classifiers can be used to detect and block the establishment of a connection between bots and their command-and-control server. Classification-as-a-service (CaaS) can separate the classification of domain names from the need for real-world training data, which are difficult to obtain but mandatory for well performing classifiers. However, domain names as well as trained models may contain privacy-critical information which should not be leaked to either the model provider or the data provider. Several generic frameworks for privacy-preserving machine learning (ML) have been proposed in the past that can preserve data and model privacy. Thus, it seems high time to combine state-of-the-art DGA classifiers and privacy-preservation frameworks to enable privacy-preserving CaaS, preserving both, data and model privacy for the DGA detection use case. In this work, we examine the real-world applicability of four generic frameworks for privacy-preserving ML using different state-of-the-art DGA detection models. Our results show that out-of-the-box DGA detection models are computationally infeasible for privacy-preserving inference in a real-world setting. We propose model simplifications that achieve a reduction in inference latency of up to 95%, and up to 97% in communication complexity while causing an accuracy penalty of less than 0.17%. Despite this significant improvement, real-time classification is still not feasible in a traditional two-party setting. Thus, more efficient secure multi-party computation (SMPC) or homomorphic encryption (HE) schemes are required to enable real-world feasibility of privacy-preserving CaaS for DGA detection.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115791523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Towards Change Detection in Privacy Policies with Natural Language Processing 基于自然语言处理的隐私策略变化检测

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647767

Andrick Adhikari, Rinku Dewri

{"title":"Towards Change Detection in Privacy Policies with Natural Language Processing","authors":"Andrick Adhikari, Rinku Dewri","doi":"10.1109/PST52912.2021.9647767","DOIUrl":"https://doi.org/10.1109/PST52912.2021.9647767","url":null,"abstract":"Privacy policies notify users about the privacy practices of websites, mobile apps, and other products and services. However, users rarely read them and struggle to understand their contents. Due to the complicated nature of these documents, it gets even harder to understand and take note of any changes of interest or concern when the policies are changed or revised. With advances in machine learning and natural language processing, tools that can automatically annotate sentences of policies have been developed. These annotations can help a user identify and understand relevant parts of a privacy policy. In this paper, we present our attempt to further such annotations by also detecting the important changes that occurred across sentences. Using supervised machine learning models, word-embedding, similarity matching, and structural analysis of sentences, we present a process that takes two different versions of a privacy policy as input, matches the sentences of one version to another based on semantic similarity, and identifies relevant changes between two matched sentences. We present the results and insights of applying our approach on 79 privacy policies manually downloaded from Facebook, WhatsApp, Twitter, Google, LinkedIn and Snapchat, ranging between the period of 1999 to 2020.","PeriodicalId":144610,"journal":{"name":"2021 18th International Conference on Privacy, Security and Trust (PST)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Light-Weight Active Security for Detecting DDoS Attacks in Containerised ICPS 轻量级主动安全检测容器化ICPS中的DDoS攻击

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647782

Farzana Zahid, Matthew M. Y. Kuo, R. Sinha

引用次数: 2

SegmentPerturb: Effective Black-Box Hidden Voice Attack on Commercial ASR Systems via Selective Deletion SegmentPerturb:通过选择性删除对商用ASR系统进行有效的黑箱隐藏语音攻击

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647775

Ganyu Wang, Miguel Vargas Martin

引用次数: 0

User Identification in Online Social Networks using Graph Transformer Networks 基于图转换网络的在线社交网络用户识别

2021 18th International Conference on Privacy, Security and Trust (PST) Pub Date : 2021-12-13 DOI: 10.1109/PST52912.2021.9647749

K. N. P. Kumar, M. Gavrilova

引用次数: 1