Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium最新文献_第6页

Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder 基于联邦生成自编码器的保密性高维数据采集

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0024

Xue Jiang, Xuebing Zhou, Jens Grossklags

{"title":"Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder","authors":"Xue Jiang, Xuebing Zhou, Jens Grossklags","doi":"10.2478/popets-2022-0024","DOIUrl":"https://doi.org/10.2478/popets-2022-0024","url":null,"abstract":"Abstract Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility. In this paper, we aim at addressing the curse-of-dimensionality problem in LDP-based high-dimensional data collection. Based on the idea of machine learning and data synthesis, we propose DP-Fed-Wae, an efficient privacy-preserving framework for collecting high-dimensional categorical data. With the combination of a generative autoencoder, federated learning, and differential privacy, our framework is capable of privately learning the statistical distributions of local data and generating high utility synthetic data on the server side without revealing users’ private information. We have evaluated the framework in terms of data utility and privacy protection on a number of real-world datasets containing 68–124 classification attributes. We show that our framework outperforms the LDP-based baseline algorithms in capturing joint distributions and correlations of attributes and generating high-utility synthetic data. With a local privacy guarantee ∈ = 8, the machine learning models trained with the synthetic data generated by the baseline algorithm cause an accuracy loss of 10% ~ 30%, whereas the accuracy loss is significantly reduced to less than 3% and at best even less than 1% with our framework. Extensive experimental results demonstrate the capability and efficiency of our framework in synthesizing high-dimensional data while striking a satisfactory utility-privacy balance.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"481 - 500"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46243940","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: 9

Multiparty Reach and Frequency Histogram: Private, Secure, and Practical 多方覆盖和频率直方图:私密、安全、实用

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0019

Badih Ghazi, Ben Kreuter, Ravi Kumar, Pasin Manurangsi, Jiayu Peng, E. Skvortsov, Yao Wang, Craig Wright

引用次数: 6

From “Onion Not Found” to Guard Discovery 从"找不到洋葱"到"发现守卫

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0026

Lennart Oldenburg, Gunes Acar, C. Díaz

{"title":"From “Onion Not Found” to Guard Discovery","authors":"Lennart Oldenburg, Gunes Acar, C. Díaz","doi":"10.2478/popets-2022-0026","DOIUrl":"https://doi.org/10.2478/popets-2022-0026","url":null,"abstract":"Abstract We present a novel web-based attack that identifies a Tor user’s guard in a matter of seconds. Our attack is low-cost, fast, and stealthy. It requires only a moderate amount of resources and can be deployed by website owners, third-party script providers, and malicious exits—if the website traffic is unencrypted. The attack works by injecting resources from non-existing onion service addresses into a webpage. Upon visiting the attack webpage with Tor Browser, the victim’s Tor client creates many circuits to look up the non-existing addresses. This allows middle relays controlled by the adversary to detect the distinctive traffic pattern of the “404 Not Found” lookups and identify the victim’s guard. We evaluate our attack with extensive simulations and live Tor network measurements, taking a range of victim machine, network, and geolocation configurations into account. We find that an adversary running a small number of HSDirs and providing 5 % of Tor’s relay bandwidth needs 12.06 seconds to identify the guards of 50 % of the victims, while it takes 22.01 seconds to discover 90 % of the victims’ guards. Finally, we evaluate a set of countermeasures against our attack including a defense that we develop based on a token bucket and the recently proposed Vanguards-lite defense in Tor.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"522 - 543"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42957166","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

Setting the Bar Low: Are Websites Complying With the Minimum Requirements of the CCPA? 降低门槛：网站是否符合CCPA的最低要求？

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0030

Maggie Van Nortwick, Christo Wilson

{"title":"Setting the Bar Low: Are Websites Complying With the Minimum Requirements of the CCPA?","authors":"Maggie Van Nortwick, Christo Wilson","doi":"10.2478/popets-2022-0030","DOIUrl":"https://doi.org/10.2478/popets-2022-0030","url":null,"abstract":"Abstract On June 28, 2018, the California State Legislature passed the California Consumer Privacy Act (CCPA), arguably the most comprehensive piece of online privacy legislation in the United States. Online services covered by the CCPA are required to provide a hyperlink on their homepage with the text “Do Not Sell My Personal Information” (DNSMPI). The CCPA went into effect on January 1, 2020, a date that was chosen to give data collectors time to study the new law and bring themselves into compliance. In this study, we begin the process of investigating whether websites are complying with the CCPA by focusing on DNSMPI links. Using longitudinal data crawled from the top 1M websites in the Tranco ranking, we examine which websites are including DNSMPI links, whether the websites without DNSMPI links are out of compliance with the law, whether websites are using geofences to dynamically hide DNSMPI links from non-Californians, how DNSMPI adoption has changed over time, and how websites are choosing to present DNSMPI links (e.g., in terms of font size, color, and placement). We argue that the answers to these questions are critical for spurring enforcement actions under the law, and helping to shape future privacy laws and regulations, e.g., rule making that will soon commence around the successor to the CCPA, known as the CPRA.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"608 - 628"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45064154","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: 10

Forward and Backward-Secure Range-Searchable Symmetric Encryption 前向和后向安全范围可搜索对称加密

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0003

Jiafan Wang, Sherman S. M. Chow

{"title":"Forward and Backward-Secure Range-Searchable Symmetric Encryption","authors":"Jiafan Wang, Sherman S. M. Chow","doi":"10.2478/popets-2022-0003","DOIUrl":"https://doi.org/10.2478/popets-2022-0003","url":null,"abstract":"Abstract Dynamic searchable symmetric encryption (DSSE) allows a client to query or update an outsourced encrypted database. Range queries are commonly needed. Previous range-searchable schemes either do not support updates natively (SIGMOD’16) or use file indexes of many long bit-vectors for distinct keywords, which only support toggling updates via homomorphically flipping the presence bit. (ESORICS’18). We propose a generic upgrade of any (inverted-index) DSSE to support range queries (a.k.a. range DSSE), without homomorphic encryption, and a specific instantiation with a new trade-off reducing client-side storage. Our schemes achieve forward security, an important property that mitigates file injection attacks. Moreover, we identify a variant of injection attacks against the first somewhat dynamic scheme (ESORICS’18). We also extend the definition of backward security to range DSSE and show that our schemes are compatible with a generic upgrade of backward security (CCS’17). We comprehensively analyze the computation and communication overheads, including implementation details of client-side index-related operations omitted by prior schemes. We show high empirical efficiency for million-scale databases over a million-scale keyword space.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"28 - 48"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43613224","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: 27

How Can and Would People Protect From Online Tracking? 人们如何保护自己免受在线跟踪?

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0006

M. Mehrnezhad, Kovila P. L. Coopamootoo, Ehsan Toreini

{"title":"How Can and Would People Protect From Online Tracking?","authors":"M. Mehrnezhad, Kovila P. L. Coopamootoo, Ehsan Toreini","doi":"10.2478/popets-2022-0006","DOIUrl":"https://doi.org/10.2478/popets-2022-0006","url":null,"abstract":"Abstract Online tracking is complex and users find it challenging to protect themselves from it. While the academic community has extensively studied systems and users for tracking practices, the link between the data protection regulations, websites’ practices of presenting privacy-enhancing technologies (PETs), and how users learn about PETs and practice them is not clear. This paper takes a multidimensional approach to find such a link. We conduct a study to evaluate the 100 top EU websites, where we find that information about PETs is provided far beyond the cookie notice. We also find that opting-out from privacy settings is not as easy as opting-in and becomes even more difficult (if not impossible) when the user decides to opt-out of previously accepted privacy settings. In addition, we conduct an online survey with 614 participants across three countries (UK, France, Germany) to gain a broad understanding of users’ tracking protection practices. We find that users mostly learn about PETs for tracking protection via their own research or with the help of family and friends. We find a disparity between what websites offer as tracking protection and the ways individuals report to do so. Observing such a disparity sheds light on why current policies and practices are ineffective in supporting the use of PETs by users.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"105 - 125"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48410923","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: 12

Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF 基于放宽批量OPPRF的线性复杂度电路psi

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0018

Nishanth Chandran, Divya Gupta, Akash Shah

引用次数: 28

The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites 适应方法在提高社交网站用户参与度和隐私保护方面的有效性

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0031

M. Namara, Henry Sloan, Bart P. Knijnenburg

{"title":"The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites","authors":"M. Namara, Henry Sloan, Bart P. Knijnenburg","doi":"10.2478/popets-2022-0031","DOIUrl":"https://doi.org/10.2478/popets-2022-0031","url":null,"abstract":"Abstract Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"629 - 648"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47242930","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: 4

Privacy-preserving FairSwap: Fairness and privacy interplay 隐私保护公平交换：公平与隐私的相互作用

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0021

S. Avizheh, Preston Haffey, R. Safavi-Naini

{"title":"Privacy-preserving FairSwap: Fairness and privacy interplay","authors":"S. Avizheh, Preston Haffey, R. Safavi-Naini","doi":"10.2478/popets-2022-0021","DOIUrl":"https://doi.org/10.2478/popets-2022-0021","url":null,"abstract":"Abstract Fair exchange protocols are among the most important cryptographic primitives in electronic commerce. A basic fair exchange protocol requires that two parties who want to exchange their digital items either receive what they have been promised, or lose nothing. Privacy of fair exchange requires that no one else (other than the two parties) learns anything about the items. Fairness and privacy have been considered as two distinct properties of an exchange protocol. In this paper, we show that subtle ways of leaking the exchange item to the third parties affect fairness in fair exchange protocols when the item is confidential. Our focus is on Fair-Swap, a recently proposed fair exchange protocol that uses a smart contract for dispute resolution, has proven security in UC (Universal Composability) framework, and provides privacy when both parties are honest. We demonstrate, however, that FairSwap’s dispute resolution protocol leaks information to the public and this leakage provides opportunities for the dishonest parties to influence the protocol’s fairness guarantee. We then propose an efficient privacy-enhanced version of Fair-Swap, prove its security and give an implementation and performance evaluation of our proposed system. Our privacy enhancement uses circuit randomization, and we prove its security and privacy in an extension of universal composability model for non-monolithic adversaries that would be of independent interest.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"417 - 439"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44074670","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

Masking Feedforward Neural Networks Against Power Analysis Attacks 屏蔽前馈神经网络抵御功率分析攻击

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2021-11-20 DOI: 10.2478/popets-2022-0025

Konstantinos Athanasiou, T. Wahl, A. Ding, Yunsi Fei

{"title":"Masking Feedforward Neural Networks Against Power Analysis Attacks","authors":"Konstantinos Athanasiou, T. Wahl, A. Ding, Yunsi Fei","doi":"10.2478/popets-2022-0025","DOIUrl":"https://doi.org/10.2478/popets-2022-0025","url":null,"abstract":"Abstract Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"501 - 521"},"PeriodicalIF":0.0,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69252427","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: 5