Secure search looks for and retrieves records from a (possibly cloud-hosted) encrypted database while ensuring the confidentiality of the queries. Researchers are paying increasing attention to secure search in recent years due to the growing concerns about database privacy. However, the low efficiency of (especially multiplicative) homomorphic operations in secure search has hindered its deployment in practice. To address this issue, Akavia et al. [CCS 2018, PETS 2019] proposed new protocols that bring down the number of multiplications in the search algorithm from O(n2) to O(n log2 n), and then to O(n log n), where n is the size of the database. In this paper, we present the first secure search protocol -- LEAF and its variant LEAF+ -- which only requires $O(n)$ multiplications. Specifically, at the core of LEAF are three novel methods we propose, referred to as Localization, Extraction, and Reconstruction. In addition, LEAF enjoys low communication complexity and only requires the client to perform decryption, which adds its advantage in deployment on weak-power devices such as mobile phones.
{"title":"LEAF: A Faster Secure Search Algorithm via Localization, Extraction, and Reconstruction","authors":"Rui Wen, Yu Yu, Xiang Xie, Yang Zhang","doi":"10.1145/3372297.3417237","DOIUrl":"https://doi.org/10.1145/3372297.3417237","url":null,"abstract":"Secure search looks for and retrieves records from a (possibly cloud-hosted) encrypted database while ensuring the confidentiality of the queries. Researchers are paying increasing attention to secure search in recent years due to the growing concerns about database privacy. However, the low efficiency of (especially multiplicative) homomorphic operations in secure search has hindered its deployment in practice. To address this issue, Akavia et al. [CCS 2018, PETS 2019] proposed new protocols that bring down the number of multiplications in the search algorithm from O(n2) to O(n log2 n), and then to O(n log n), where n is the size of the database. In this paper, we present the first secure search protocol -- LEAF and its variant LEAF+ -- which only requires $O(n)$ multiplications. Specifically, at the core of LEAF are three novel methods we propose, referred to as Localization, Extraction, and Reconstruction. In addition, LEAF enjoys low communication complexity and only requires the client to perform decryption, which adds its advantage in deployment on weak-power devices such as mobile phones.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74458233","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}
The Web has become a platform in which sites rely on intricate interactions that span across the boundaries of origins. While the Same-Origin Policy prevents direct data exchange with documents from other origins, the postMessage API offers one relaxation that allows developers to exchange data across these boundaries. While prior manual analysis could show the presence of issues within postMessage handlers, unfortunately, a steep increase in postMessage usage makes any manual approach intractable. To deal with this increased work load, we set out to automatically find issues in postMessage handlers that allow an attacker to execute code in the vulnerable sites, alter client-side state, or leak sensitive information. To achieve this goal, we present an automated analysis framework running inside the browser, which uses selective forced execution paired with lightweight dynamic taint tracking to find traces in the analyzed handlers that end in sinks allowing for code-execution or state alterations. We use path constraints extracted from the program traces and augment them with Exploit Templates, i.e., additional constraints, ascertaining that a valid assignment that solves all these constraints produces a code-invoking or state-manipulating behavior. Based on these constraints, we use Z3 to generate postMessages aimed at triggering the insecure functionality to prove exploitability, and validate our findings at scale. We use this framework to conduct the most comprehensive experiment studying the security issues of postMessage handlers found throughout the top 100,000 most influential sites yet, which allows us to find potentially exploitable data flows in 252 unique handlers out of which 111 were automatically exploitable.
{"title":"PMForce: Systematically Analyzing postMessage Handlers at Scale","authors":"Marius Steffens, Ben Stock","doi":"10.1145/3372297.3417267","DOIUrl":"https://doi.org/10.1145/3372297.3417267","url":null,"abstract":"The Web has become a platform in which sites rely on intricate interactions that span across the boundaries of origins. While the Same-Origin Policy prevents direct data exchange with documents from other origins, the postMessage API offers one relaxation that allows developers to exchange data across these boundaries. While prior manual analysis could show the presence of issues within postMessage handlers, unfortunately, a steep increase in postMessage usage makes any manual approach intractable. To deal with this increased work load, we set out to automatically find issues in postMessage handlers that allow an attacker to execute code in the vulnerable sites, alter client-side state, or leak sensitive information. To achieve this goal, we present an automated analysis framework running inside the browser, which uses selective forced execution paired with lightweight dynamic taint tracking to find traces in the analyzed handlers that end in sinks allowing for code-execution or state alterations. We use path constraints extracted from the program traces and augment them with Exploit Templates, i.e., additional constraints, ascertaining that a valid assignment that solves all these constraints produces a code-invoking or state-manipulating behavior. Based on these constraints, we use Z3 to generate postMessages aimed at triggering the insecure functionality to prove exploitability, and validate our findings at scale. We use this framework to conduct the most comprehensive experiment studying the security issues of postMessage handlers found throughout the top 100,000 most influential sites yet, which allows us to find potentially exploitable data flows in 252 unique handlers out of which 111 were automatically exploitable.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"59 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75340652","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}
{"title":"Session details: Session 3D: Formal Methods","authors":"Deepak Garg","doi":"10.1145/3432970","DOIUrl":"https://doi.org/10.1145/3432970","url":null,"abstract":"","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"218 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78421588","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}
We analyze the multi-user security of the streaming encryption in Google's Tink library via an extended version of the framework of nonce-based online authenticated encryption of Hoang et al. (CRYPTO'15) to support random-access decryption. We show that Tink's design choice of using random nonces and a nonce-based key-derivation function indeed improves the concrete security bound. We then give two better alternatives that are more robust against randomness failure. In addition, we show how to efficiently instantiate the key-derivation function via AES, instead of relying on HMAC-SHA256 like the current design in Tink. To accomplish this we give a multi-user analysis of the XOR-of-permutation construction of Bellare, Krovetz, and Rogaway (EUROCRYPT'98).
{"title":"Security of Streaming Encryption in Google's Tink Library","authors":"V. Hoang, Yaobin Shen","doi":"10.1145/3372297.3417273","DOIUrl":"https://doi.org/10.1145/3372297.3417273","url":null,"abstract":"We analyze the multi-user security of the streaming encryption in Google's Tink library via an extended version of the framework of nonce-based online authenticated encryption of Hoang et al. (CRYPTO'15) to support random-access decryption. We show that Tink's design choice of using random nonces and a nonce-based key-derivation function indeed improves the concrete security bound. We then give two better alternatives that are more robust against randomness failure. In addition, we show how to efficiently instantiate the key-derivation function via AES, instead of relying on HMAC-SHA256 like the current design in Tink. To accomplish this we give a multi-user analysis of the XOR-of-permutation construction of Bellare, Krovetz, and Rogaway (EUROCRYPT'98).","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75122691","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}
Providing functionality that streamlines the more tedious aspects of website interaction is of paramount importance to browsers as it can significantly improve the overall user experience. Browsers' autofill functionality exemplifies this goal, as it alleviates the burden of repetitively typing the same information across websites. At the same time, however, it also presents a significant privacy risk due to the inherent disparity between the browser's interpretation of a given web page and what users can visually perceive. In this paper we present the first, to our knowledge, comprehensive exploration of the privacy threats of autofill functionality. We first develop a series of new techniques for concealing the presence of form elements that allow us to obtain sensitive user information while bypassing existing browser defenses. Alarmingly, our large-scale study in the Alexa top 100K reveals the widespread use of such deceptive techniques for stealthily obtaining user-identifying information, as they are present in at least 5.8% of the forms that are autofilled by Chrome. Subsequently, our in-depth investigation of browsers' autofill functionality reveals a series of flaws and idiosyncrasies, which we exploit through a series of novel attack vectors that target specific aspects of browsers' behavior. By chaining these together we are able to demonstrate a novel invasive side-channel attack that exploits browser's autofill preview functionality for inferring sensitive information even when users choose to not utilize autofill. This attack affects all major Chromium-based browsers and allows attackers to probe users' autofill profiles for over a hundred thousand candidate values (e.g., credit card and phone numbers). Overall, while the preview mode is intended as a protective measure for enabling more informed decisions, ultimately it creates a new avenue of exposure that circumvents a user's choice to not divulge their information. In light of our findings, we have disclosed our techniques to the affected vendors, and have also created a Chrome extension that can prevent our attacks and mitigate this threat until our countermeasures are incorporated into browsers.
{"title":"Fill in the Blanks: Empirical Analysis of the Privacy Threats of Browser Form Autofill","authors":"Xu Lin, Panagiotis Ilia, Jason Polakis","doi":"10.1145/3372297.3417271","DOIUrl":"https://doi.org/10.1145/3372297.3417271","url":null,"abstract":"Providing functionality that streamlines the more tedious aspects of website interaction is of paramount importance to browsers as it can significantly improve the overall user experience. Browsers' autofill functionality exemplifies this goal, as it alleviates the burden of repetitively typing the same information across websites. At the same time, however, it also presents a significant privacy risk due to the inherent disparity between the browser's interpretation of a given web page and what users can visually perceive. In this paper we present the first, to our knowledge, comprehensive exploration of the privacy threats of autofill functionality. We first develop a series of new techniques for concealing the presence of form elements that allow us to obtain sensitive user information while bypassing existing browser defenses. Alarmingly, our large-scale study in the Alexa top 100K reveals the widespread use of such deceptive techniques for stealthily obtaining user-identifying information, as they are present in at least 5.8% of the forms that are autofilled by Chrome. Subsequently, our in-depth investigation of browsers' autofill functionality reveals a series of flaws and idiosyncrasies, which we exploit through a series of novel attack vectors that target specific aspects of browsers' behavior. By chaining these together we are able to demonstrate a novel invasive side-channel attack that exploits browser's autofill preview functionality for inferring sensitive information even when users choose to not utilize autofill. This attack affects all major Chromium-based browsers and allows attackers to probe users' autofill profiles for over a hundred thousand candidate values (e.g., credit card and phone numbers). Overall, while the preview mode is intended as a protective measure for enabling more informed decisions, ultimately it creates a new avenue of exposure that circumvents a user's choice to not divulge their information. In light of our findings, we have disclosed our techniques to the affected vendors, and have also created a Chrome extension that can prevent our attacks and mitigate this threat until our countermeasures are incorporated into browsers.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79370820","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}
We propose a new type of vulnerability for Robotic Vehicles (RVs), called Cyber-Physical Inconsistency. These vulnerabilities target safety checks in RVs (e.g., crash detection). They can be exploited by setting up malicious environment conditions such as placing an obstacle with a certain weight and a certain angle in the RV's trajectory. Once exploited, the safety checks may fail to report real physical accidents or report false alarms (while the RV is still operating normally). Both situations could lead to life-threatening consequences. The root cause of such vulnerabilities is that existing safety checks are mostly using simple range checks implemented in general-purpose programming languages, which are incapable of describing the complex and delicate physical world. We develop a novel technique that requires the interplay of program analysis, vehicle modeling, and search-based testing to identify such vulnerabilities. Our experiment on 4 real-world control software and 8 vehicles including quadrotors, rover, and fixed-wing airplane has discovered 10 real vulnerabilities. Our technique does not have false positives as it only reports when an exploit can be generated.
{"title":"Cyber-Physical Inconsistency Vulnerability Identification for Safety Checks in Robotic Vehicles","authors":"Hongjun Choi, Sayali Kate, Yousra Aafer, X. Zhang, Dongyan Xu","doi":"10.1145/3372297.3417249","DOIUrl":"https://doi.org/10.1145/3372297.3417249","url":null,"abstract":"We propose a new type of vulnerability for Robotic Vehicles (RVs), called Cyber-Physical Inconsistency. These vulnerabilities target safety checks in RVs (e.g., crash detection). They can be exploited by setting up malicious environment conditions such as placing an obstacle with a certain weight and a certain angle in the RV's trajectory. Once exploited, the safety checks may fail to report real physical accidents or report false alarms (while the RV is still operating normally). Both situations could lead to life-threatening consequences. The root cause of such vulnerabilities is that existing safety checks are mostly using simple range checks implemented in general-purpose programming languages, which are incapable of describing the complex and delicate physical world. We develop a novel technique that requires the interplay of program analysis, vehicle modeling, and search-based testing to identify such vulnerabilities. Our experiment on 4 real-world control software and 8 vehicles including quadrotors, rover, and fixed-wing airplane has discovered 10 real vulnerabilities. Our technique does not have false positives as it only reports when an exploit can be generated.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85206137","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}
Local Privilege Escalation (LPE) is a common attack vector used by attackers to gain higher-level permissions. In this poster, we present a system called LPET to mine LPE vulnerabilities of third-party software in MS-Windows. Our insight is that the LPE is often caused by the interactions between high-privilege processes and user-controllable files. The interactions include creating a file, starting a process and others. Based on this observation, LPET first monitors software behaviors and constructs a directed interaction graph to abstract entities, such as files and processes, and their interactions. Then LPET analyzes exploiting paths from the graph by extracting user-controllable entities and checking their privileges. Finally, LPET verifies the exploiting paths using replacement or hijacking attacks. In the preliminary experiments, LPET found vulnerabilities in various software. Moreover, we discovered a common weakness pattern that some components were executed by software with high privilege after being released in the user-controllable temporary directory during installation, update, and uninstallation. By replacing the components, attackers with low privilege can hijack the execution flow of software to execute their codes with high privilege. We found that a wide range of software suffers from this weakness pattern, including Cisco AnyConnect, Dropbox, Notepad++.
{"title":"LPET -- Mining MS-Windows Software Privilege Escalation Vulnerabilities by Monitoring Interactive Behavior","authors":"Can Huang, Xinhui Han, Guorui Yu","doi":"10.1145/3372297.3420014","DOIUrl":"https://doi.org/10.1145/3372297.3420014","url":null,"abstract":"Local Privilege Escalation (LPE) is a common attack vector used by attackers to gain higher-level permissions. In this poster, we present a system called LPET to mine LPE vulnerabilities of third-party software in MS-Windows. Our insight is that the LPE is often caused by the interactions between high-privilege processes and user-controllable files. The interactions include creating a file, starting a process and others. Based on this observation, LPET first monitors software behaviors and constructs a directed interaction graph to abstract entities, such as files and processes, and their interactions. Then LPET analyzes exploiting paths from the graph by extracting user-controllable entities and checking their privileges. Finally, LPET verifies the exploiting paths using replacement or hijacking attacks. In the preliminary experiments, LPET found vulnerabilities in various software. Moreover, we discovered a common weakness pattern that some components were executed by software with high privilege after being released in the user-controllable temporary directory during installation, update, and uninstallation. By replacing the components, attackers with low privilege can hijack the execution flow of software to execute their codes with high privilege. We found that a wide range of software suffers from this weakness pattern, including Cisco AnyConnect, Dropbox, Notepad++.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77070892","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}
C. Cremers, Jaiden Fairoze, B. Kiesl, Aurora Naska
We investigate whether modern messaging apps achieve the strong post-compromise security guarantees offered by their underlying protocols. In particular, we perform a black-box experiment in which a user becomes the victim of a clone attack; in this attack, the user's full state (including identity keys) is compromised by an attacker who clones their device and then later attempts to impersonate them, using the app through its user interface. Our attack should be prevented by protocols that offer post-compromise security, and thus, by all apps that are based on Signal's double-ratchet algorithm (for instance, the Signal app, WhatsApp, and Facebook Secret Conversations). Our experiments reveal that this is not the case: most deployed messaging apps fall far short of the security that their underlying mechanisms suggest. We conjecture that this security gap is a result of many apps trading security for usability, by tolerating certain forms of desynchronization. We show that the tolerance of desynchronization necessarily leads to loss of post-compromise security in the strict sense, but we also show that more security can be retained than is currently offered in practice. Concretely, we present a modified version of the double-ratchet algorithm that tolerates forms of desynchronization while still being able to detect cloning activity. Moreover, we formally analyze our algorithm using the Tamarin prover to show that it achieves the desired security properties.
{"title":"Clone Detection in Secure Messaging: Improving Post-Compromise Security in Practice","authors":"C. Cremers, Jaiden Fairoze, B. Kiesl, Aurora Naska","doi":"10.1145/3372297.3423354","DOIUrl":"https://doi.org/10.1145/3372297.3423354","url":null,"abstract":"We investigate whether modern messaging apps achieve the strong post-compromise security guarantees offered by their underlying protocols. In particular, we perform a black-box experiment in which a user becomes the victim of a clone attack; in this attack, the user's full state (including identity keys) is compromised by an attacker who clones their device and then later attempts to impersonate them, using the app through its user interface. Our attack should be prevented by protocols that offer post-compromise security, and thus, by all apps that are based on Signal's double-ratchet algorithm (for instance, the Signal app, WhatsApp, and Facebook Secret Conversations). Our experiments reveal that this is not the case: most deployed messaging apps fall far short of the security that their underlying mechanisms suggest. We conjecture that this security gap is a result of many apps trading security for usability, by tolerating certain forms of desynchronization. We show that the tolerance of desynchronization necessarily leads to loss of post-compromise security in the strict sense, but we also show that more security can be retained than is currently offered in practice. Concretely, we present a modified version of the double-ratchet algorithm that tolerates forms of desynchronization while still being able to detect cloning activity. Moreover, we formally analyze our algorithm using the Tamarin prover to show that it achieves the desired security properties.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82039347","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}
The creation and distribution of child sexual abuse materials (CSAM) involves a continuing violation of the victims? privacy beyond the original harms they document. A large volume of these materials is distributed via the Freenet anonymity network: in our observations, nearly one third of requests on Freenet were for known CSAM. In this paper, we propose and evaluate a novel approach for investigating these violations of exploited childrens' privacy. Our forensic method distinguishes whether or not a neighboring peer is the actual uploader or downloader of a file or merely a relayer. Our method requires analysis of the traffic sent to a single, passive node only. We evaluate our method extensively. Our in situ measurements of actual CSAM requests show an FPR of 0.002 ± 0.003 for identifying downloaders. And we show an FPR of 0.009 ± 0.018, a precision of 1.00 ± 0.01, and a TPR of 0.44 ± 0.01 for identifying uploaders based on in situ tests. Further, we derive expressions for the FPR and Power of our hypothesis test; perform simulations of single and concurrent downloaders; and characterize the Freenet network to inform parameter selection. We were participants in several United States Federal Court cases in which the use of our method was uniformly upheld.
{"title":"A Forensically Sound Method of Identifying Downloaders and Uploaders in Freenet","authors":"B. Levine, M. Liberatore, Brian Lynn, M. Wright","doi":"10.1145/3372297.3417876","DOIUrl":"https://doi.org/10.1145/3372297.3417876","url":null,"abstract":"The creation and distribution of child sexual abuse materials (CSAM) involves a continuing violation of the victims? privacy beyond the original harms they document. A large volume of these materials is distributed via the Freenet anonymity network: in our observations, nearly one third of requests on Freenet were for known CSAM. In this paper, we propose and evaluate a novel approach for investigating these violations of exploited childrens' privacy. Our forensic method distinguishes whether or not a neighboring peer is the actual uploader or downloader of a file or merely a relayer. Our method requires analysis of the traffic sent to a single, passive node only. We evaluate our method extensively. Our in situ measurements of actual CSAM requests show an FPR of 0.002 ± 0.003 for identifying downloaders. And we show an FPR of 0.009 ± 0.018, a precision of 1.00 ± 0.01, and a TPR of 0.44 ± 0.01 for identifying uploaders based on in situ tests. Further, we derive expressions for the FPR and Power of our hypothesis test; perform simulations of single and concurrent downloaders; and characterize the Freenet network to inform parameter selection. We were participants in several United States Federal Court cases in which the use of our method was uniformly upheld.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85431300","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}
{"title":"Session details: Session 6C: Side Channels","authors":"Yinqian Zhang","doi":"10.1145/3432984","DOIUrl":"https://doi.org/10.1145/3432984","url":null,"abstract":"","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79971584","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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