Pub Date : 2023-01-01DOI: 10.56553/popets-2023-0026
Túlio Pascoal, Jérémie Decouchant, Antoine Boutet, Marcus Völp
Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.
{"title":"I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies","authors":"Túlio Pascoal, Jérémie Decouchant, Antoine Boutet, Marcus Völp","doi":"10.56553/popets-2023-0026","DOIUrl":"https://doi.org/10.56553/popets-2023-0026","url":null,"abstract":"Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135237088","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}
Pub Date : 2023-01-01DOI: 10.56553/popets-2023-0032
Yuting Zhan, Hamed Haddadi, Afra Mashhadi
As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).
{"title":"Privacy-Aware Adversarial Network in Human Mobility Prediction","authors":"Yuting Zhan, Hamed Haddadi, Afra Mashhadi","doi":"10.56553/popets-2023-0032","DOIUrl":"https://doi.org/10.56553/popets-2023-0032","url":null,"abstract":"As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135420123","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}
Pub Date : 2023-01-01DOI: 10.56553/popets-2023-0030
Ahmed Roushdy Elkordy, Jiang Zhang, Yahya H. Ezzeldin, Konstantinos Psounis, Salman Avestimehr
Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users’ devices, privacy still cannot be guaranteed since significant computations on users’ training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates.While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage as the number of training rounds increases. We also observe similar dependencies for the FedAvg and FedProx protocol.
{"title":"How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?","authors":"Ahmed Roushdy Elkordy, Jiang Zhang, Yahya H. Ezzeldin, Konstantinos Psounis, Salman Avestimehr","doi":"10.56553/popets-2023-0030","DOIUrl":"https://doi.org/10.56553/popets-2023-0030","url":null,"abstract":"Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users’ devices, privacy still cannot be guaranteed since significant computations on users’ training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates.While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage as the number of training rounds increases. We also observe similar dependencies for the FedAvg and FedProx protocol.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136258713","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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0046
Karel Kubicek, Jakob Merane, C. C. Jiménez, A. Stremitzer, S. Bechtold, D. Basin
Abstract The sending of marketing emails is regulated to protect users from unsolicited emails. For instance, the European Union’s ePrivacy Directive states that marketers must obtain users’ prior consent, and the General Data Protection Regulation (GDPR) specifies further that such consent must be freely given, specific, informed, and unambiguous. Based on these requirements, we design a labeling of legal characteristics for websites and emails. This leads to a simple decision procedure that detects potential legal violations. Using our procedure, we evaluated 1000 websites and the 5000 emails resulting from registering to these websites. Both datasets and evaluations are available upon request. We find that 21.9% of the websites contain potential violations of privacy and unfair competition rules, either in the registration process (17.3%) or email communication (17.7%). We demonstrate with a statistical analysis the possibility of automatically detecting such potential violations.
{"title":"Checking Websites’ GDPR Consent Compliance for Marketing Emails","authors":"Karel Kubicek, Jakob Merane, C. C. Jiménez, A. Stremitzer, S. Bechtold, D. Basin","doi":"10.2478/popets-2022-0046","DOIUrl":"https://doi.org/10.2478/popets-2022-0046","url":null,"abstract":"Abstract The sending of marketing emails is regulated to protect users from unsolicited emails. For instance, the European Union’s ePrivacy Directive states that marketers must obtain users’ prior consent, and the General Data Protection Regulation (GDPR) specifies further that such consent must be freely given, specific, informed, and unambiguous. Based on these requirements, we design a labeling of legal characteristics for websites and emails. This leads to a simple decision procedure that detects potential legal violations. Using our procedure, we evaluated 1000 websites and the 5000 emails resulting from registering to these websites. Both datasets and evaluations are available upon request. We find that 21.9% of the websites contain potential violations of privacy and unfair competition rules, either in the registration process (17.3%) or email communication (17.7%). We demonstrate with a statistical analysis the possibility of automatically detecting such potential violations.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43602394","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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0036
Jan Tolsdorf, D. Reinhardt, Luigi Lo Iacono
Abstract The processing of employees’ personal data is dramatically increasing, yet there is a lack of tools that allow employees to manage their privacy. In order to develop these tools, one needs to understand what sensitive personal data are and what factors influence employees’ willingness to disclose. Current privacy research, however, lacks such insights, as it has focused on other contexts in recent decades. To fill this research gap, we conducted a cross-sectional survey with 553 employees from Germany. Our survey provides multiple insights into the relationships between perceived data sensitivity and willingness to disclose in the employment context. Among other things, we show that the perceived sensitivity of certain types of data differs substantially from existing studies in other contexts. Moreover, currently used legal and contextual distinctions between different types of data do not accurately reflect the subtleties of employees’ perceptions. Instead, using 62 different data elements, we identified four groups of personal data that better reflect the multi-dimensionality of perceptions. However, previously found common disclosure antecedents in the context of online privacy do not seem to affect them. We further identified three groups of employees that differ in their perceived data sensitivity and willingness to disclose, but neither in their privacy beliefs nor in their demographics. Our findings thus provide employers, policy makers, and researchers with a better understanding of employees’ privacy perceptions and serve as a basis for future targeted research on specific types of personal data and employees.
{"title":"Employees’ privacy perceptions: exploring the dimensionality and antecedents of personal data sensitivity and willingness to disclose","authors":"Jan Tolsdorf, D. Reinhardt, Luigi Lo Iacono","doi":"10.2478/popets-2022-0036","DOIUrl":"https://doi.org/10.2478/popets-2022-0036","url":null,"abstract":"Abstract The processing of employees’ personal data is dramatically increasing, yet there is a lack of tools that allow employees to manage their privacy. In order to develop these tools, one needs to understand what sensitive personal data are and what factors influence employees’ willingness to disclose. Current privacy research, however, lacks such insights, as it has focused on other contexts in recent decades. To fill this research gap, we conducted a cross-sectional survey with 553 employees from Germany. Our survey provides multiple insights into the relationships between perceived data sensitivity and willingness to disclose in the employment context. Among other things, we show that the perceived sensitivity of certain types of data differs substantially from existing studies in other contexts. Moreover, currently used legal and contextual distinctions between different types of data do not accurately reflect the subtleties of employees’ perceptions. Instead, using 62 different data elements, we identified four groups of personal data that better reflect the multi-dimensionality of perceptions. However, previously found common disclosure antecedents in the context of online privacy do not seem to affect them. We further identified three groups of employees that differ in their perceived data sensitivity and willingness to disclose, but neither in their privacy beliefs nor in their demographics. Our findings thus provide employers, policy makers, and researchers with a better understanding of employees’ privacy perceptions and serve as a basis for future targeted research on specific types of personal data and employees.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43118224","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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0044
Rakibul Hasan, Mario Fritz
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.
{"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":null,"pages":null},"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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0048
Domien Schepers, Aanjhan Ranganathan
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).
{"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":null,"pages":null},"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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0054
Valerie Fetzer, Marcel Keller, Sven Maier, Markus Raiber, Andy Rupp, Rebecca Schwerdt
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.
{"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":null,"pages":null},"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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0035
Mansoor Ahmed-Rengers, Diana A. Vasile, Daniel Hugenroth, A. Beresford, Ross Anderson
Abstract Whistleblowing is hazardous in a world of pervasive surveillance, yet many leading newspapers expect sources to contact them with methods that are either insecure or barely usable. In an attempt to do better, we conducted two workshops with British news organisations and surveyed whistleblowing options and guidelines at major media outlets. We concluded that the soft spot is a system for initial contact and trust establishment between sources and reporters. CoverDrop is a two-way, secure system to do this. We support secure messaging within a news app, so that all its other users provide cover traffic, which we channel through a threshold mix instantiated in a Trusted Execution Environment within the news organisation. CoverDrop is designed to resist a powerful global adversary with the ability to issue warrants against infrastructure providers, yet it can easily be integrated into existing infrastructure. We present the results from our workshops, describe CoverDrop’s design and demonstrate its security and performance.
{"title":"CoverDrop: Blowing the Whistle Through A News App","authors":"Mansoor Ahmed-Rengers, Diana A. Vasile, Daniel Hugenroth, A. Beresford, Ross Anderson","doi":"10.2478/popets-2022-0035","DOIUrl":"https://doi.org/10.2478/popets-2022-0035","url":null,"abstract":"Abstract Whistleblowing is hazardous in a world of pervasive surveillance, yet many leading newspapers expect sources to contact them with methods that are either insecure or barely usable. In an attempt to do better, we conducted two workshops with British news organisations and surveyed whistleblowing options and guidelines at major media outlets. We concluded that the soft spot is a system for initial contact and trust establishment between sources and reporters. CoverDrop is a two-way, secure system to do this. We support secure messaging within a news app, so that all its other users provide cover traffic, which we channel through a threshold mix instantiated in a Trusted Execution Environment within the news organisation. CoverDrop is designed to resist a powerful global adversary with the ability to issue warrants against infrastructure providers, yet it can easily be integrated into existing infrastructure. We present the results from our workshops, describe CoverDrop’s design and demonstrate its security and performance.","PeriodicalId":74556,"journal":{"name":"Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48583083","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}
Pub Date : 2022-03-03DOI: 10.2478/popets-2022-0040
Peter Story, Daniel Smullen, Rex Chen, Yaxing Yao, A. Acquisti, Lorrie Faith Cranor, N. Sadeh, F. Schaub
Abstract Browsing privacy tools can help people protect their digital privacy. However, tools which provide the strongest protections—such as Tor Browser—have struggled to achieve widespread adoption. This may be due to usability challenges, misconceptions, behavioral biases, or mere lack of awareness. In this study, we test the effectiveness of nudging interventions that encourage the adoption of Tor Browser. First, we test an informational nudge based on protection motivation theory (PMT), designed to raise awareness of Tor Browser and help participants form accurate perceptions of it. Next, we add an action planning implementation intention, designed to help participants identify opportunities for using Tor Browser. Finally, we add a coping planning implementation intention, designed to help participants overcome challenges to using Tor Browser, such as extreme website slowness. We test these nudges in a longitudinal field experiment with 537 participants. We find that our PMT-based intervention increased use of Tor Browser in both the short- and long-term. Our coping planning nudge also increased use of Tor Browser, but only in the week following our intervention. We did not find statistically significant evidence of our action planning nudge increasing use of Tor Browser. Our study contributes to a greater understanding of factors influencing the adoption of Tor Browser, and how nudges might be used to encourage the adoption of Tor Browser and similar privacy enhancing technologies.
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