Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium最新文献_第3页

Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring 通过因果分析和对抗性审查了解教育技术中人口统计数据的效用和隐私性

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0044

Rakibul Hasan, Mario Fritz

{"title":"Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring","authors":"Rakibul Hasan, Mario Fritz","doi":"10.2478/popets-2022-0044","DOIUrl":"https://doi.org/10.2478/popets-2022-0044","url":null,"abstract":"Abstract Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does not causally affect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"245 - 262"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44121355","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 Positioning in Wi-Fi Fine Timing Measurement Wi-Fi精细定时测量中的隐私保护定位

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0048

Domien Schepers, Aanjhan Ranganathan

{"title":"Privacy-Preserving Positioning in Wi-Fi Fine Timing Measurement","authors":"Domien Schepers, Aanjhan Ranganathan","doi":"10.2478/popets-2022-0048","DOIUrl":"https://doi.org/10.2478/popets-2022-0048","url":null,"abstract":"Abstract With the standardization of Wi-Fi Fine Timing Measurement (Wi-Fi FTM; IEEE 802.11mc), the IEEE introduced indoor positioning for Wi-Fi networks. To date, Wi-Fi FTM is the most widely supported Wi-Fi distance measurement and positioning system. In this paper, we perform the first privacy analysis of Wi-Fi FTM and evaluate devices from a wide variety of vendors. We find the protocol inherently leaks location-sensitive information. Most notably, we present techniques that allow any client to be localized and tracked by a solely passive adversary. We identify flaws inWi-Fi FTM MAC address randomization and present techniques to fingerprint stations with firmware-specific granularity further leaking client identity. We address these shortcomings and present a privacy-preserving passive positioning system that leverages existing Wi-Fi FTM infrastructure and requires no hardware changes. Due to the absence of any client-side transmission, our design hides the very existence of a client and as a side-effect improves overall scalability without compromising on accuracy. Finally, we present privacy-enhancing recommendations for the current and next-generation protocols such as Wi-Fi Next Generation Positioning (Wi-Fi NGP; IEEE 802.11az).","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"325 - 343"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41611085","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

PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics PUBA:隐私保护用户数据簿记和分析

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0054

Valerie Fetzer, Marcel Keller, Sven Maier, Markus Raiber, Andy Rupp, Rebecca Schwerdt

{"title":"PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics","authors":"Valerie Fetzer, Marcel Keller, Sven Maier, Markus Raiber, Andy Rupp, Rebecca Schwerdt","doi":"10.2478/popets-2022-0054","DOIUrl":"https://doi.org/10.2478/popets-2022-0054","url":null,"abstract":"Abstract In this paper we propose Privacy-preserving User-data Bookkeeping & Analytics (PUBA), a building block destined to enable the implementation of business models (e.g., targeted advertising) and regulations (e.g., fraud detection) requiring user-data analysis in a privacy-preserving way. In PUBA, users keep an unlinkable but authenticated cryptographic logbook containing their historic data on their device. This logbook can only be updated by the operator while its content is not revealed. Users can take part in a privacy-preserving analytics computation, where it is ensured that their logbook is up-to-date and authentic while the potentially secret analytics function is verified to be privacy-friendly. Taking constrained devices into account, users may also outsource analytic computations (to a potentially malicious proxy not colluding with the operator).We model our novel building block in the Universal Composability framework and provide a practical protocol instantiation. To demonstrate the flexibility of PUBA, we sketch instantiations of privacy-preserving fraud detection and targeted advertising, although it could be used in many more scenarios, e.g. data analytics for multi-modal transportation systems. We implemented our bookkeeping protocols and an exemplary outsourced analytics computation based on logistic regression using the MP-SPDZ MPC framework. Performance evaluations using a smartphone as user device and more powerful hardware for operator and proxy suggest that PUBA for smaller logbooks can indeed be practical.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"447 - 516"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43610131","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

CoverDrop: Blowing the Whistle Through A News App CoverDrop：通过新闻应用程序吹口哨

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0035

Mansoor Ahmed-Rengers, Diana A. Vasile, Daniel Hugenroth, A. Beresford, Ross Anderson

引用次数: 1

Understanding Privacy-Related Advice on Stack Overflow 了解有关堆栈溢出的隐私相关建议

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0038

Mohammad Tahaei, Tianshi Li, Kami Vaniea

{"title":"Understanding Privacy-Related Advice on Stack Overflow","authors":"Mohammad Tahaei, Tianshi Li, Kami Vaniea","doi":"10.2478/popets-2022-0038","DOIUrl":"https://doi.org/10.2478/popets-2022-0038","url":null,"abstract":"Abstract Privacy tasks can be challenging for developers, resulting in privacy frameworks and guidelines from the research community which are designed to assist developers in considering privacy features and applying privacy enhancing technologies in early stages of software development. However, how developers engage with privacy design strategies is not yet well understood. In this work, we look at the types of privacy-related advice developers give each other and how that advice maps to Hoepman’s privacy design strategies. We qualitatively analyzed 119 privacy-related accepted answers on Stack Overflow from the past five years and extracted 148 pieces of advice from these answers. We find that the advice is mostly around compliance with regulations and ensuring confidentiality with a focus on the inform, hide, control, and minimize of the Hoepman’s privacy design strategies. Other strategies, abstract, separate, enforce, and demonstrate, are rarely advised. Answers often include links to official documentation and online articles, highlighting the value of both official documentation and other informal materials such as blog posts. We make recommendations for promoting the under-stated strategies through tools, and detail the importance of providing better developer support to handle third-party data practices.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"114 - 131"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47937492","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: 20

Revisiting Identification Issues in GDPR ‘Right Of Access’ Policies: A Technical and Longitudinal Analysis 重新审视GDPR“访问权”政策中的识别问题:技术和纵向分析

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0037

Mariano Di Martino, Isaac Meers, P. Quax, Kenneth M. Andries, W. Lamotte

{"title":"Revisiting Identification Issues in GDPR ‘Right Of Access’ Policies: A Technical and Longitudinal Analysis","authors":"Mariano Di Martino, Isaac Meers, P. Quax, Kenneth M. Andries, W. Lamotte","doi":"10.2478/popets-2022-0037","DOIUrl":"https://doi.org/10.2478/popets-2022-0037","url":null,"abstract":"Abstract Several data protection regulations permit individuals to request all personal information that an organization holds about them by utilizing Subject Access Requests (SARs). Prior work has observed the identification process of such requests, demonstrating weak policies that are vulnerable to potential data breaches. In this paper, we analyze and compare prior work in terms of methodologies, requested identification credentials and threat models in the context of privacy and cybersecurity. Furthermore, we have devised a longitudinal study in which we examine the impact of responsible disclosures by re-evaluating the SAR authentication processes of 40 organizations after they had two years to improve their policies. Here, we demonstrate that 53% of the previously vulnerable organizations have not corrected their policy and an additional 27% of previously non-vulnerable organizations have potentially weakened their policies instead of improving them, thus leaking sensitive personal information to potential adversaries. To better understand state-of-the-art SAR policies, we interviewed several Data Protection Officers and explored the reasoning behind their processes from a viewpoint in the industry and gained insights about potential criminal abuse of weak SAR policies. Finally, we propose several technical modifications to SAR policies that reduce privacy and security risks of data controllers.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"95 - 113"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41733982","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

Who Knows I Like Jelly Beans? An Investigation Into Search Privacy 谁知道我喜欢果冻豆？搜索隐私调查

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0053

Daniel Kats, David Silva, Johann Roturier

{"title":"Who Knows I Like Jelly Beans? An Investigation Into Search Privacy","authors":"Daniel Kats, David Silva, Johann Roturier","doi":"10.2478/popets-2022-0053","DOIUrl":"https://doi.org/10.2478/popets-2022-0053","url":null,"abstract":"Abstract Internal site search is an integral part of how users navigate modern sites, from restaurant reservations to house hunting to searching for medical solutions. Search terms on these sites may contain sensitive information such as location, medical information, or sexual preferences; when further coupled with a user’s IP address or a browser’s user agent string, this information can become very specific, and in some cases possibly identifying. In this paper, we measure the various ways by which search terms are sent to third parties when a user submits a search query. We developed a methodology for identifying and interacting with search components, which we implemented on top of an instrumented headless browser. We used this crawler to visit the Tranco top one million websites and analyzed search term leakage across three vectors: URL query parameters, payloads, and the Referer HTTP header. Our crawler found that 512,701 of the top 1 million sites had internal site search. We found that 81.3% of websites containing internal site search sent (or leaked from a user’s perspective) our search terms to third parties in some form. We then compared our results to the expected results based on a natural language analysis of the privacy policies of those leaking websites (where available) and found that about 87% of those privacy policies do not mention search terms explicitly. However, about 75% of these privacy policies seem to mention the sharing of some information with third-parties in a generic manner. We then present a few countermeasures, including a browser extension to warn users about imminent search term leakage to third parties. We conclude this paper by making recommendations on clarifying the privacy implications of internal site search to end users.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"426 - 446"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48923575","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

Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction 垂直联邦学习预测过程中隐私泄露的综合分析

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0045

Xue Jiang, Xuebing Zhou, Jens Grossklags

{"title":"Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction","authors":"Xue Jiang, Xuebing Zhou, Jens Grossklags","doi":"10.2478/popets-2022-0045","DOIUrl":"https://doi.org/10.2478/popets-2022-0045","url":null,"abstract":"Abstract Vertical federated learning (VFL), a variant of federated learning, has recently attracted increasing attention. An active party having the true labels jointly trains a model with other parties (referred to as passive parties) in order to use more features to achieve higher model accuracy. During the prediction phase, all the parties collaboratively compute the predicted confidence scores of each target record and the results will be finally returned to the active party. However, a recent study by Luo et al. [28] pointed out that the active party can use these confidence scores to reconstruct passive-party features and cause severe privacy leakage. In this paper, we conduct a comprehensive analysis of privacy leakage in VFL frameworks during the prediction phase. Our study improves on previous work [28] regarding two aspects. We first design a general gradient-based reconstruction attack framework that can be flexibly applied to simple logistic regression models as well as multi-layer neural networks. Moreover, besides performing the attack under the white-box setting, we give the first attempt to conduct the attack under the black-box setting. Extensive experiments on a number of real-world datasets show that our proposed attack is effective under different settings and can achieve at best twice or thrice of a reduction of attack error compared to previous work [28]. We further analyze a list of potential mitigation approaches and compare their privacy-utility performances. Experimental results demonstrate that privacy leakage from the confidence scores is a substantial privacy risk in VFL frameworks during the prediction phase, which cannot be simply solved by crypto-based confidentiality approaches. On the other hand, processing the confidence scores with information compression and randomization approaches can provide strengthened privacy protection.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"263 - 281"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48930456","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: 25

Efficient Set Membership Proofs using MPC-in-the-Head 在头部使用MPC的有效集成员证明

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0047

Aarushi Goel, M. Green, Mathias Hall-Andersen, Gabriel Kaptchuk

{"title":"Efficient Set Membership Proofs using MPC-in-the-Head","authors":"Aarushi Goel, M. Green, Mathias Hall-Andersen, Gabriel Kaptchuk","doi":"10.2478/popets-2022-0047","DOIUrl":"https://doi.org/10.2478/popets-2022-0047","url":null,"abstract":"Abstract Set membership proofs are an invaluable part of privacy preserving systems. These proofs allow a prover to demonstrate knowledge of a witness w corresponding to a secret element x of a public set, such that they jointly satisfy a given NP relation, i.e. ℛ(w, x) = 1 and x is a member of a public set {x1, . . . , x𝓁}. This allows the identity of the prover to remain hidden, eg. ring signatures and confidential transactions in cryptocurrencies. In this work, we develop a new technique for efficiently adding logarithmic-sized set membership proofs to any MPC-in-the-head based zero-knowledge protocol (Ishai et al. [STOC’07]). We integrate our technique into an open source implementation of the state-of-the-art, post quantum secure zero-knowledge protocol of Katz et al. [CCS’18].We find that using our techniques to construct ring signatures results in signatures (based only on symmetric key primitives) that are between 5 and 10 times smaller than state-of-the-art techniques based on the same assumptions. We also show that our techniques can be used to efficiently construct post-quantum secure RingCT from only symmetric key primitives.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"304 - 324"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43386179","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

Updatable Private Set Intersection 可更新的专用集交集

Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Pub Date : 2022-03-03 DOI: 10.2478/popets-2022-0051

S. Badrinarayanan, Peihan Miao, Tiancheng Xie

{"title":"Updatable Private Set Intersection","authors":"S. Badrinarayanan, Peihan Miao, Tiancheng Xie","doi":"10.2478/popets-2022-0051","DOIUrl":"https://doi.org/10.2478/popets-2022-0051","url":null,"abstract":"Abstract Private set intersection (PSI) allows two mutually distrusting parties each with a set as input, to learn the intersection of both their sets without revealing anything more about their respective input sets. Traditionally, PSI studies the static setting where the computation is performed only once on both parties’ input sets. We initiate the study of updatable private set intersection (UPSI), which allows parties to compute the intersection of their private sets on a regular basis with sets that also constantly get updated. We consider two specific settings. In the first setting called UPSI with addition, parties can add new elements to their old sets. We construct two protocols in this setting, one allowing both parties to learn the output and the other only allowing one party to learn the output. In the second setting called UPSI with weak deletion, parties can additionally delete their old elements every t days. We present a protocol for this setting allowing both parties to learn the output. All our protocols are secure against semi-honest adversaries and have the guarantee that both the computational and communication complexity only grow with the set updates instead of the entire sets. Finally, we implement our UPSI with addition protocols and compare with the state-of-the-art PSI protocols. Our protocols compare favorably when the total set size is sufficiently large, the new updates are sufficiently small, or in networks with low bandwidth.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":"2022 1","pages":"378 - 406"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41971170","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