P. Antonino, Wojciech Aleksander Wołoszyn, A. W. Roscoe
Modern processors can offer hardware primitives that allow a process to run in isolation. These primitives implement a trusted execution environment (TEE) in which a program can run such that the integrity and confidentiality of its execution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example of such primitives and its isolated processes are called enclaves. These guarantees, however, can be easily thwarted if the enclave has not been properly designed. Its interface with the untrusted software stack is a perhaps the largest attack surface that adversaries can exploit; unintended interactions with untrusted code can expose the enclave to memory corruption attacks, for instance. In this paper, we propose a notion of an orderly enclave which splits its behaviour into the following execution phases: entry, secure, ocall, and exit. Each of them imposes a set of restrictions that enforce a particular policy of access to untrusted memory and, in some cases, sanitisation conditions. A violation of these policies and conditions might indicate an undesired interaction with untrusted data/code or a lack of sanitisation, both of which can be harnessed to perpetrate attacks against the enclave. We also introduce Guardian: an open-source tool that uses symbolic execution to carry out the validation of an enclave against our notion of an orderly enclave; in this process, it also looks for some other typical attack primitives. We discuss how our approach can prevent and flag enclave vulnerabilities that have been identified in the literature. Moreover, we have evaluated how our approach fares in the analysis of some enclave samples. In this process, Guardian identified some security issues previously undetected in some of these samples that were acknowledged and fixed by the corresponding maintainers.
{"title":"Guardian: Symbolic Validation of Orderliness in SGX Enclaves","authors":"P. Antonino, Wojciech Aleksander Wołoszyn, A. W. Roscoe","doi":"10.1145/3474123.3486755","DOIUrl":"https://doi.org/10.1145/3474123.3486755","url":null,"abstract":"Modern processors can offer hardware primitives that allow a process to run in isolation. These primitives implement a trusted execution environment (TEE) in which a program can run such that the integrity and confidentiality of its execution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example of such primitives and its isolated processes are called enclaves. These guarantees, however, can be easily thwarted if the enclave has not been properly designed. Its interface with the untrusted software stack is a perhaps the largest attack surface that adversaries can exploit; unintended interactions with untrusted code can expose the enclave to memory corruption attacks, for instance. In this paper, we propose a notion of an orderly enclave which splits its behaviour into the following execution phases: entry, secure, ocall, and exit. Each of them imposes a set of restrictions that enforce a particular policy of access to untrusted memory and, in some cases, sanitisation conditions. A violation of these policies and conditions might indicate an undesired interaction with untrusted data/code or a lack of sanitisation, both of which can be harnessed to perpetrate attacks against the enclave. We also introduce Guardian: an open-source tool that uses symbolic execution to carry out the validation of an enclave against our notion of an orderly enclave; in this process, it also looks for some other typical attack primitives. We discuss how our approach can prevent and flag enclave vulnerabilities that have been identified in the literature. Moreover, we have evaluated how our approach fares in the analysis of some enclave samples. In this process, Guardian identified some security issues previously undetected in some of these samples that were acknowledged and fixed by the corresponding maintainers.","PeriodicalId":109533,"journal":{"name":"Proceedings of the 2021 on Cloud Computing Security Workshop","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121044642","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}
Mahnush Movahedi, Benjamin M. Case, James Honaker, Andrew Knox, Li Li, Yiming Paul Li, Sanjay Saravanan, Shubho Sengupta, Erik Taubeneck
Randomized Controlled Trials, when feasible, give the strongest and most trustworthy empirical measures of causal effects. They are the gold standard in many clinical, social, and behavioral fields of study. However, the most important settings often involve the most sensitive data, therefore cause privacy concerns. In this paper, we outline a way to deploy an end-to-end privacy-preserving protocol for learning causal effects from Randomized Controlled Trials (RCTs). We are particularly focused on the difficult and important case where one party determines which treatment an individual receives, and another party measures outcomes on individuals, and these parties do not want to leak any of their information to each other, but still want to collectively learn a true causal effect in the world. Moreover, we show how such a protocol can be scaled to 500 million rows of data and more than a billion gates. We also offer an open source deployment of this protocol. We accomplish this by a three-stage solution, interconnecting and blending three privacy technologies--private set intersection, multiparty computation, and differential privacy--to address core points of privacy leakage, at the join, at the point of computation, and at the release, respectively. The first stage uses the Private-ID protocol[8] to create a private encrypted join of the users. The second stage utilizes the encrypted join to run multiple instances of a general purpose MPC over a sharded database to aggregate statistics about each experimental group while discarding individuals who took an action before they received treatment. The third stage adds distributed and calibrated Differential Privacy (DP) noise within the final MPC computations to the released aggregate statistical estimates of causal effects and their uncertainty measures, providing formal two-sided privacy guarantees. We also evaluate the performance of multiple open source general purpose MPC libraries for this task. We additionally demonstrate how we have used this to create a working ads effectiveness measurement product capable of measuring hundreds of millions of individuals per experiment.
{"title":"Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment","authors":"Mahnush Movahedi, Benjamin M. Case, James Honaker, Andrew Knox, Li Li, Yiming Paul Li, Sanjay Saravanan, Shubho Sengupta, Erik Taubeneck","doi":"10.1145/3474123.3486764","DOIUrl":"https://doi.org/10.1145/3474123.3486764","url":null,"abstract":"Randomized Controlled Trials, when feasible, give the strongest and most trustworthy empirical measures of causal effects. They are the gold standard in many clinical, social, and behavioral fields of study. However, the most important settings often involve the most sensitive data, therefore cause privacy concerns. In this paper, we outline a way to deploy an end-to-end privacy-preserving protocol for learning causal effects from Randomized Controlled Trials (RCTs). We are particularly focused on the difficult and important case where one party determines which treatment an individual receives, and another party measures outcomes on individuals, and these parties do not want to leak any of their information to each other, but still want to collectively learn a true causal effect in the world. Moreover, we show how such a protocol can be scaled to 500 million rows of data and more than a billion gates. We also offer an open source deployment of this protocol. We accomplish this by a three-stage solution, interconnecting and blending three privacy technologies--private set intersection, multiparty computation, and differential privacy--to address core points of privacy leakage, at the join, at the point of computation, and at the release, respectively. The first stage uses the Private-ID protocol[8] to create a private encrypted join of the users. The second stage utilizes the encrypted join to run multiple instances of a general purpose MPC over a sharded database to aggregate statistics about each experimental group while discarding individuals who took an action before they received treatment. The third stage adds distributed and calibrated Differential Privacy (DP) noise within the final MPC computations to the released aggregate statistical estimates of causal effects and their uncertainty measures, providing formal two-sided privacy guarantees. We also evaluate the performance of multiple open source general purpose MPC libraries for this task. We additionally demonstrate how we have used this to create a working ads effectiveness measurement product capable of measuring hundreds of millions of individuals per experiment.","PeriodicalId":109533,"journal":{"name":"Proceedings of the 2021 on Cloud Computing Security Workshop","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123842481","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}
Claudio Canella, M. Werner, D. Gruss, Michael Schwarz
Software vulnerabilities undermine the security of applications. By blocking unused functionality, the impact of potential exploits can be reduced. While seccomp provides a solution for filtering syscalls, it requires manual implementation of filter rules for each individual application. Recent work has investigated approaches to automate this task. However, as we show, these approaches make assumptions that are not necessary or require overly time-consuming analysis. In this paper, we propose Chestnut, an automated approach for generating strict syscall filters with lower requirements and limitations. Chestnut comprises two phases, with the first phase consisting of two static components, i.e., a compiler and a binary analyzer, that statically extract the used syscalls. The compiler-based approach of Chestnut is up to factor 73 faster than previous approaches with the same accuracy. On the binary level, our approach extends over previous ones by also applying to non-PIC binaries. An optional second phase of Chestnut is dynamic refinement to restrict the set of allowed syscalls further. We demonstrate that Chestnut on average blocks 302 syscalls (86.5%) via the compiler and 288 (82.5%) using the binary analysis on a set of 18 applications. Chestnut blocks the dangerous exec syscall in 50% and 77.7% of the tested applications using the compiler- and binary-based approach, respectively. For the tested applications, Chestnut blocks exploitation of more than 61% of the 175 CVEs that target the kernel via syscalls.
{"title":"Automating Seccomp Filter Generation for Linux Applications","authors":"Claudio Canella, M. Werner, D. Gruss, Michael Schwarz","doi":"10.1145/3474123.3486762","DOIUrl":"https://doi.org/10.1145/3474123.3486762","url":null,"abstract":"Software vulnerabilities undermine the security of applications. By blocking unused functionality, the impact of potential exploits can be reduced. While seccomp provides a solution for filtering syscalls, it requires manual implementation of filter rules for each individual application. Recent work has investigated approaches to automate this task. However, as we show, these approaches make assumptions that are not necessary or require overly time-consuming analysis. In this paper, we propose Chestnut, an automated approach for generating strict syscall filters with lower requirements and limitations. Chestnut comprises two phases, with the first phase consisting of two static components, i.e., a compiler and a binary analyzer, that statically extract the used syscalls. The compiler-based approach of Chestnut is up to factor 73 faster than previous approaches with the same accuracy. On the binary level, our approach extends over previous ones by also applying to non-PIC binaries. An optional second phase of Chestnut is dynamic refinement to restrict the set of allowed syscalls further. We demonstrate that Chestnut on average blocks 302 syscalls (86.5%) via the compiler and 288 (82.5%) using the binary analysis on a set of 18 applications. Chestnut blocks the dangerous exec syscall in 50% and 77.7% of the tested applications using the compiler- and binary-based approach, respectively. For the tested applications, Chestnut blocks exploitation of more than 61% of the 175 CVEs that target the kernel via syscalls.","PeriodicalId":109533,"journal":{"name":"Proceedings of the 2021 on Cloud Computing Security Workshop","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124739213","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}
Xianrui Meng, D. Papadopoulos, Alina Oprea, Nikos Triandopoulos
In collaborative learning, multiple parties contribute their datasets to jointly deduce global machine learning models for numerous predictive tasks. Despite its efficacy, this learning paradigm fails to encompass critical application domains that involve highly sensitive data, such as healthcare and security analytics, where privacy risks limit entities to individually train models using only their own datasets. In this work, we target privacy-preserving collaborative hierarchical clustering. We introduce a formal security definition that aims to achieve balance between utility and privacy and present a two-party protocol that provably satisfies it. We then extend our protocol with: (i) an optimized version for single-linkage clustering, and (ii) scalable approximation variants. We implement all our schemes and experimentally evaluate their performance and accuracy on synthetic and real datasets, obtaining very encouraging results. For example, end-to-end execution of our secure approximate protocol for over 1M 10-dimensional data samples requires 35sec of computation and achieves 97.09% accuracy.
{"title":"Private Hierarchical Clustering and Efficient Approximation","authors":"Xianrui Meng, D. Papadopoulos, Alina Oprea, Nikos Triandopoulos","doi":"10.1145/3474123.3486760","DOIUrl":"https://doi.org/10.1145/3474123.3486760","url":null,"abstract":"In collaborative learning, multiple parties contribute their datasets to jointly deduce global machine learning models for numerous predictive tasks. Despite its efficacy, this learning paradigm fails to encompass critical application domains that involve highly sensitive data, such as healthcare and security analytics, where privacy risks limit entities to individually train models using only their own datasets. In this work, we target privacy-preserving collaborative hierarchical clustering. We introduce a formal security definition that aims to achieve balance between utility and privacy and present a two-party protocol that provably satisfies it. We then extend our protocol with: (i) an optimized version for single-linkage clustering, and (ii) scalable approximation variants. We implement all our schemes and experimentally evaluate their performance and accuracy on synthetic and real datasets, obtaining very encouraging results. For example, end-to-end execution of our secure approximate protocol for over 1M 10-dimensional data samples requires 35sec of computation and achieves 97.09% accuracy.","PeriodicalId":109533,"journal":{"name":"Proceedings of the 2021 on Cloud Computing Security Workshop","volume":"212 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941283","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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