Pub Date : 2023-05-01DOI: 10.1109/SP46215.2023.10179320
Marcel Fourné, Dominik Wermke, W. Enck, S. Fahl, Y. Acar
The 2020 Solarwinds attack was a tipping point that caused a heightened awareness about the security of the software supply chain and in particular the large amount of trust placed in build systems. Reproducible Builds (R-Bs) provide a strong foundation to build defenses for arbitrary attacks against build systems by ensuring that given the same source code, build environment, and build instructions, bitwise-identical artifacts are created. Unfortunately, much of the software industry believes R-Bs are too far out of reach for most projects. The goal of this paper is to help identify a path for R-Bs to become a commonplace property.To this end, we conducted a series of 24 semi-structured expert interviews with participants from the Reproducible-Builds.org project, finding that self-effective work by highly motivated developers and collaborative communication with upstream projects are key contributors to R-Bs. We identified a range of motivations that can encourage open source developers to strive for R-Bs, including indicators of quality, security benefits, and more efficient caching of artifacts. We also identify experiences that help and hinder adoption, which often revolves around communication with upstream projects. We conclude with recommendations on how to better integrate R-Bs with the efforts of the open source and free software community.
{"title":"It’s like flossing your teeth: On the Importance and Challenges of Reproducible Builds for Software Supply Chain Security","authors":"Marcel Fourné, Dominik Wermke, W. Enck, S. Fahl, Y. Acar","doi":"10.1109/SP46215.2023.10179320","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179320","url":null,"abstract":"The 2020 Solarwinds attack was a tipping point that caused a heightened awareness about the security of the software supply chain and in particular the large amount of trust placed in build systems. Reproducible Builds (R-Bs) provide a strong foundation to build defenses for arbitrary attacks against build systems by ensuring that given the same source code, build environment, and build instructions, bitwise-identical artifacts are created. Unfortunately, much of the software industry believes R-Bs are too far out of reach for most projects. The goal of this paper is to help identify a path for R-Bs to become a commonplace property.To this end, we conducted a series of 24 semi-structured expert interviews with participants from the Reproducible-Builds.org project, finding that self-effective work by highly motivated developers and collaborative communication with upstream projects are key contributors to R-Bs. We identified a range of motivations that can encourage open source developers to strive for R-Bs, including indicators of quality, security benefits, and more efficient caching of artifacts. We also identify experiences that help and hinder adoption, which often revolves around communication with upstream projects. We conclude with recommendations on how to better integrate R-Bs with the efforts of the open source and free software community.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237578","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179470
Jack Doerner, Yashvanth Kondi, Eysa Lee, Abhi Shelat, LaKyah Tyner
We propose a secure multiparty signing protocol for the BBS+ signature scheme; in other words, an anonymous credential scheme with threshold issuance. We prove that due to the structure of the BBS+ signature, simply verifying the signature produced by an otherwise semi-honest protocol is sufficient to achieve composable security against a malicious adversary. Consequently, our protocol is extremely simple and efficient: it involves a single request from the client (who requires a signature) to the signing parties, two exchanges of messages among the signing parties, and finally a response to the client; in some deployment scenarios the concrete cost bottleneck may be the client’s local verification of the signature that it receives. Furthermore, our protocol can be extended to support the strongest form of blind signing and to serve as a distributed evaluation protocol for the Dodis-Yampolskiy Oblivious VRF. We validate our efficiency claims by implementing and benchmarking our protocol.
{"title":"Threshold BBS+ Signatures for Distributed Anonymous Credential Issuance","authors":"Jack Doerner, Yashvanth Kondi, Eysa Lee, Abhi Shelat, LaKyah Tyner","doi":"10.1109/SP46215.2023.10179470","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179470","url":null,"abstract":"We propose a secure multiparty signing protocol for the BBS+ signature scheme; in other words, an anonymous credential scheme with threshold issuance. We prove that due to the structure of the BBS+ signature, simply verifying the signature produced by an otherwise semi-honest protocol is sufficient to achieve composable security against a malicious adversary. Consequently, our protocol is extremely simple and efficient: it involves a single request from the client (who requires a signature) to the signing parties, two exchanges of messages among the signing parties, and finally a response to the client; in some deployment scenarios the concrete cost bottleneck may be the client’s local verification of the signature that it receives. Furthermore, our protocol can be extended to support the strongest form of blind signing and to serve as a distributed evaluation protocol for the Dodis-Yampolskiy Oblivious VRF. We validate our efficiency claims by implementing and benchmarking our protocol.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126495483","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}
Acoustic eavesdropping targeting private or confidential spaces is one of the most severe privacy threats. Soundproof rooms may reduce such risks, but they cannot prevent sophisticated eavesdropping, which has been an emerging research trend in recent years. Researchers have investigated such acoustic eavesdropping attacks via sensor-enabled side-channels. However, such attacks either make unrealistic assumptions or have considerable constraints. This paper introduces mmEcho, an acoustic eavesdropping system that uses a millimeter-wave radio signal to accurately measure the micrometer-level vibration of an object induced by sound waves. Compared with previous works, our eavesdropping method is highly accurate and requires no prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from moving sources at various distances, orientations, reverberating objects, sound insulators, spoken languages, and sound levels.
{"title":"mmEcho: A mmWave-based Acoustic Eavesdropping Method","authors":"Pengfei Hu, Wenhao Li, Riccardo Spolaor, Xiuzhen Cheng","doi":"10.1109/SP46215.2023.10179484","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179484","url":null,"abstract":"Acoustic eavesdropping targeting private or confidential spaces is one of the most severe privacy threats. Soundproof rooms may reduce such risks, but they cannot prevent sophisticated eavesdropping, which has been an emerging research trend in recent years. Researchers have investigated such acoustic eavesdropping attacks via sensor-enabled side-channels. However, such attacks either make unrealistic assumptions or have considerable constraints. This paper introduces mmEcho, an acoustic eavesdropping system that uses a millimeter-wave radio signal to accurately measure the micrometer-level vibration of an object induced by sound waves. Compared with previous works, our eavesdropping method is highly accurate and requires no prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from moving sources at various distances, orientations, reverberating objects, sound insulators, spoken languages, and sound levels.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"27 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120853730","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179288
S. Şahin, Suood Abdulaziz Al-Roomi, Tara Poteat, Frank H. Li
Passwords are the de facto standard for online authentication today, and will likely remain so for the foreseeable future. As a consequence, the security community has extensively explored how users behave with passwords, producing recommendations for password policies that promote password security and usability for users. However, it is the website administrators who must adopt such recommendations to enact improvements to online authentication in practice. To date, there has been limited investigation of how web administrators manage password policies for their sites. To improve online authentication at scale, we must understand the factors behind this specific population’s behaviors and decisions, and how to help administrators deploy more secure password policies.In this paper, we explore how web administrators determine the password policies that they employ, what considerations impact a policy’s evolution, and what challenges administrators encounter when managing a site’s policy. To do so, we conduct an online survey and in-depth semi-structured interviews with 11 US-based web administrators with direct experience managing website password policies. Through our qualitative study, we identify a small set of key factors driving the majority of password policy decisions, and barriers that inhibit administrators from enacting policies that are more aligned with modern guidelines. Moving forward, we propose directions for future research and community action that may help administrators manage password policies more effectively.
{"title":"Investigating the Password Policy Practices of Website Administrators","authors":"S. Şahin, Suood Abdulaziz Al-Roomi, Tara Poteat, Frank H. Li","doi":"10.1109/SP46215.2023.10179288","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179288","url":null,"abstract":"Passwords are the de facto standard for online authentication today, and will likely remain so for the foreseeable future. As a consequence, the security community has extensively explored how users behave with passwords, producing recommendations for password policies that promote password security and usability for users. However, it is the website administrators who must adopt such recommendations to enact improvements to online authentication in practice. To date, there has been limited investigation of how web administrators manage password policies for their sites. To improve online authentication at scale, we must understand the factors behind this specific population’s behaviors and decisions, and how to help administrators deploy more secure password policies.In this paper, we explore how web administrators determine the password policies that they employ, what considerations impact a policy’s evolution, and what challenges administrators encounter when managing a site’s policy. To do so, we conduct an online survey and in-depth semi-structured interviews with 11 US-based web administrators with direct experience managing website password policies. Through our qualitative study, we identify a small set of key factors driving the majority of password policy decisions, and barriers that inhibit administrators from enacting policies that are more aligned with modern guidelines. Moving forward, we propose directions for future research and community action that may help administrators manage password policies more effectively.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116024682","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179360
Shengwei An, Y. Yao, Qiuling Xu, Shiqing Ma, Guanhong Tao, Siyuan Cheng, Kaiyuan Zhang, Yingqi Liu, Guangyu Shen, Ian Kelk, Xiangyu Zhang
This paper presents a novel physical impersonating attack against face recognition systems. It aims at generating consistent style changes across multiple pictures of the attacker under different conditions and poses. Additionally, the style changes are required to be physically realizable by make-up and can induce the intended misclassification. To achieve the goal, we develop novel techniques to embed multiple pictures of the same physical person to vectors in the StyleGAN’s latent space, such that the embedded latent vectors have some implicit correlations to make the search for consistent style changes feasible. Our digital and physical evaluation results show our approach can allow an outsider attacker to successfully impersonate the insiders with consistent and natural changes.
{"title":"ImU: Physical Impersonating Attack for Face Recognition System with Natural Style Changes","authors":"Shengwei An, Y. Yao, Qiuling Xu, Shiqing Ma, Guanhong Tao, Siyuan Cheng, Kaiyuan Zhang, Yingqi Liu, Guangyu Shen, Ian Kelk, Xiangyu Zhang","doi":"10.1109/SP46215.2023.10179360","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179360","url":null,"abstract":"This paper presents a novel physical impersonating attack against face recognition systems. It aims at generating consistent style changes across multiple pictures of the attacker under different conditions and poses. Additionally, the style changes are required to be physically realizable by make-up and can induce the intended misclassification. To achieve the goal, we develop novel techniques to embed multiple pictures of the same physical person to vectors in the StyleGAN’s latent space, such that the embedded latent vectors have some implicit correlations to make the search for consistent style changes feasible. Our digital and physical evaluation results show our approach can allow an outsider attacker to successfully impersonate the insiders with consistent and natural changes.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128179087","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179365
Peiyuan Liu, Jeremiah Blocki, Wenjie Bai
In password security a defender would like to identify and warn users with weak passwords. Similarly, the defender may also want to predict what fraction of passwords would be cracked within B guesses as the attacker’s guessing budget B varies from small (online attacker) to large (offline attacker). Towards each of these goals the defender would like to quickly estimate the guessing number for each user password pwd assuming that the attacker uses a password cracking model M i.e., how many password guesses will the attacker check before s/he cracks each user password pwd. Since naïve brute-force enumeration can be prohibitively expensive when the guessing number is very large, Dell’Amico and Filippone [1] developed an efficient Monte Carlo algorithm to estimate the guessing number of a given password pwd. While Dell’Amico and Filippone proved that their estimator is unbiased there is no guarantee that the Monte Carlo estimates are accurate nor does the method provide confidence ranges on the estimated guessing number or even indicate if/when there is a higher degree of uncertainty.Our contributions are as follows: First, we identify theoretical examples where, with high probability, Monte Carlo Strength estimation produces highly inaccurate estimates of individual guessing numbers as well as the entire guessing curve. Second, we introduce Confident Monte Carlo Strength Estimation as an extension of Dell’Amico and Filippone [1]. Given a password our estimator generates an upper and lower bound with the guarantee that, except with probability δ, the true guessing number lies within the given confidence range. Our techniques can also be used to characterize the attacker’s guessing curve. In particular, given a probabilistic password cracking model M we can generate high confidence upper and lower bounds on the fraction of passwords that the attacker will crack as the guessing budget B varies.
{"title":"Confident Monte Carlo: Rigorous Analysis of Guessing Curves for Probabilistic Password Models","authors":"Peiyuan Liu, Jeremiah Blocki, Wenjie Bai","doi":"10.1109/SP46215.2023.10179365","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179365","url":null,"abstract":"In password security a defender would like to identify and warn users with weak passwords. Similarly, the defender may also want to predict what fraction of passwords would be cracked within B guesses as the attacker’s guessing budget B varies from small (online attacker) to large (offline attacker). Towards each of these goals the defender would like to quickly estimate the guessing number for each user password pwd assuming that the attacker uses a password cracking model M i.e., how many password guesses will the attacker check before s/he cracks each user password pwd. Since naïve brute-force enumeration can be prohibitively expensive when the guessing number is very large, Dell’Amico and Filippone [1] developed an efficient Monte Carlo algorithm to estimate the guessing number of a given password pwd. While Dell’Amico and Filippone proved that their estimator is unbiased there is no guarantee that the Monte Carlo estimates are accurate nor does the method provide confidence ranges on the estimated guessing number or even indicate if/when there is a higher degree of uncertainty.Our contributions are as follows: First, we identify theoretical examples where, with high probability, Monte Carlo Strength estimation produces highly inaccurate estimates of individual guessing numbers as well as the entire guessing curve. Second, we introduce Confident Monte Carlo Strength Estimation as an extension of Dell’Amico and Filippone [1]. Given a password our estimator generates an upper and lower bound with the guarantee that, except with probability δ, the true guessing number lies within the given confidence range. Our techniques can also be used to characterize the attacker’s guessing curve. In particular, given a probabilistic password cracking model M we can generate high confidence upper and lower bounds on the fraction of passwords that the attacker will crack as the guessing budget B varies.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128428764","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179363
Donghui Dai, Zhenlin An, Lei Yang
Recent works demonstrated that speech recognition systems or voice assistants can be manipulated by malicious voice commands, which are injected through various inaudible media, such as ultrasound, laser, and electromagnetic interference (EMI). In this work, we explore a new kind of inaudible voice attack through the magnetic interference induced by a wireless charger. Essentially, we show that the microphone components of smart devices suffer from severe magnetic interference when they are enjoying wireless charging, due to the absence of effective protection against the EMI at low frequencies (100 kHz or below). By taking advantage of this vulnerability, we design two inaudible voice attacks, HeartwormAttack and ParasiteAttack, both of which aim to inject malicious voice commands into smart devices being wirelessly charged. They make use of a compromised wireless charger or accessory equipment (called parasite) to inject the voice, respectively. We conduct extensive experiments with 17 victim devices (iPhone, Huawei, Samsung, etc.) and 6 types of voice assistants (Siri, Google STT, Bixby, etc.). Evaluation results demonstrate the feasibility of two proposed attacks with commercial charging settings.
{"title":"Inducing Wireless Chargers to Voice Out for Inaudible Command Attacks","authors":"Donghui Dai, Zhenlin An, Lei Yang","doi":"10.1109/SP46215.2023.10179363","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179363","url":null,"abstract":"Recent works demonstrated that speech recognition systems or voice assistants can be manipulated by malicious voice commands, which are injected through various inaudible media, such as ultrasound, laser, and electromagnetic interference (EMI). In this work, we explore a new kind of inaudible voice attack through the magnetic interference induced by a wireless charger. Essentially, we show that the microphone components of smart devices suffer from severe magnetic interference when they are enjoying wireless charging, due to the absence of effective protection against the EMI at low frequencies (100 kHz or below). By taking advantage of this vulnerability, we design two inaudible voice attacks, HeartwormAttack and ParasiteAttack, both of which aim to inject malicious voice commands into smart devices being wirelessly charged. They make use of a compromised wireless charger or accessory equipment (called parasite) to inject the voice, respectively. We conduct extensive experiments with 17 victim devices (iPhone, Huawei, Samsung, etc.) and 6 types of voice assistants (Siri, Google STT, Bixby, etc.). Evaluation results demonstrate the feasibility of two proposed attacks with commercial charging settings.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130892916","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179337
Erik-Oliver Blass, F. Kerschbaum
We introduce and investigate the privacy-preserving version of collaborative data cleaning. With collaborative data cleaning, two parties want to reconcile their data sets to filter out badly classified, misclassified data items. In the privacy-preserving (private) version of data cleaning, the additional security goal is that parties should only learn their misclassified data items, but nothing else about the other party’s data set. The problem of private data cleaning is essentially a variation of private set intersection (PSI), and one could employ recent circuit-PSI techniques to compute misclassifications with privacy. However, we design, analyze, and implement three new protocols tailored to the specifics of private data cleaning that outperform a circuit-PSI-based approach. With the first protocol, we exploit the idea that a small additional leakage (the differentially private size of the intersection of data items) allows for a reduction in complexity over circuit-PSI. The other two protocols convert the problem of finding a mismatch in data classifications into finding a match, and then follow the standard technique of using oblivious pseudorandom functions (OPRF) for computing PSI. Depending on the number of data classes, this leads to a concrete runtime improvement over circuit-PSI.
{"title":"Private Collaborative Data Cleaning via Non-Equi PSI","authors":"Erik-Oliver Blass, F. Kerschbaum","doi":"10.1109/SP46215.2023.10179337","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179337","url":null,"abstract":"We introduce and investigate the privacy-preserving version of collaborative data cleaning. With collaborative data cleaning, two parties want to reconcile their data sets to filter out badly classified, misclassified data items. In the privacy-preserving (private) version of data cleaning, the additional security goal is that parties should only learn their misclassified data items, but nothing else about the other party’s data set. The problem of private data cleaning is essentially a variation of private set intersection (PSI), and one could employ recent circuit-PSI techniques to compute misclassifications with privacy. However, we design, analyze, and implement three new protocols tailored to the specifics of private data cleaning that outperform a circuit-PSI-based approach. With the first protocol, we exploit the idea that a small additional leakage (the differentially private size of the intersection of data items) allows for a reduction in complexity over circuit-PSI. The other two protocols convert the problem of finding a mismatch in data classifications into finding a match, and then follow the standard technique of using oblivious pseudorandom functions (OPRF) for computing PSI. Depending on the number of data classes, this leads to a concrete runtime improvement over circuit-PSI.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134274942","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 : 2023-05-01DOI: 10.1109/SP46215.2023.10179385
Ryan Torok, A. Levy
We present Sandcastle, an entropy-based browser fingerprinting defense that aims to minimize its interference with legitimate web applications. Sandcastle allows developers to partition code that operates on identifiable information into sandboxes to prove to the browser the information cannot be sent in any network request. Meanwhile, sandboxes may make full use of identifiable information on the client side, including writing to dedicated regions of the Document Object Model. For applications where this policy is too strict, Sandcastle provides an expressive cashier that allows precise control over the granularity of data that is leaked to the network. These features allow Sandcastle to eliminate most or all of the noise added to the outputs of identifiable APIs by Chrome’s Privacy Budget framework, the current state of the art in entropy-based fingerprinting defenses. Enabling unlimited client-side use of identifiable information allows for a much more comprehensive set of web applications to run under a fingerprinting defense, such as 3D games and video streaming, and provides a mechanism to expand the space of APIs that can be introduced to the web ecosystem without sacrificing privacy.
{"title":"Only Pay for What You Leak: Leveraging Sandboxes for a Minimally Invasive Browser Fingerprinting Defense","authors":"Ryan Torok, A. Levy","doi":"10.1109/SP46215.2023.10179385","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179385","url":null,"abstract":"We present Sandcastle, an entropy-based browser fingerprinting defense that aims to minimize its interference with legitimate web applications. Sandcastle allows developers to partition code that operates on identifiable information into sandboxes to prove to the browser the information cannot be sent in any network request. Meanwhile, sandboxes may make full use of identifiable information on the client side, including writing to dedicated regions of the Document Object Model. For applications where this policy is too strict, Sandcastle provides an expressive cashier that allows precise control over the granularity of data that is leaked to the network. These features allow Sandcastle to eliminate most or all of the noise added to the outputs of identifiable APIs by Chrome’s Privacy Budget framework, the current state of the art in entropy-based fingerprinting defenses. Enabling unlimited client-side use of identifiable information allows for a much more comprehensive set of web applications to run under a fingerprinting defense, such as 3D games and video streaming, and provides a mechanism to expand the space of APIs that can be introduced to the web ecosystem without sacrificing privacy.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134270328","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}
Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using a multi-classifier framework. Each classifier, designed based on a novel transformer model, identifies a specific website using its local patterns extracted from multiple traffic segments. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale dataset collected over multiple months (by far the largest multi-tab WF dataset studied in academic papers.) The experimental results illustrate that ARES effectively achieves the multi-tab WF attack with the best F1-score of 0.907. Further, ARES remains robust even against various WF defenses.
{"title":"Robust Multi-tab Website Fingerprinting Attacks in the Wild","authors":"Xinhao Deng, Qilei Yin, Zhuotao Liu, Xiyuan Zhao, Qi Li, Mingwei Xu, Ke Xu, Jianping Wu","doi":"10.1109/SP46215.2023.10179464","DOIUrl":"https://doi.org/10.1109/SP46215.2023.10179464","url":null,"abstract":"Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using a multi-classifier framework. Each classifier, designed based on a novel transformer model, identifies a specific website using its local patterns extracted from multiple traffic segments. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale dataset collected over multiple months (by far the largest multi-tab WF dataset studied in academic papers.) The experimental results illustrate that ARES effectively achieves the multi-tab WF attack with the best F1-score of 0.907. Further, ARES remains robust even against various WF defenses.","PeriodicalId":439989,"journal":{"name":"2023 IEEE Symposium on Security and Privacy (SP)","volume":"198199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115575625","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}
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