Pub Date : 2020-05-01DOI: 10.1109/SP40000.2020.00078
Meng Xu, Sanidhya Kashyap, Hanqing Zhao, Taesoo Kim
Data races occur when two threads fail to use proper synchronization when accessing shared data. In kernel file systems, which are highly concurrent by design, data races are common mistakes and often wreak havoc on the users, causing inconsistent states or data losses. Prior fuzzing practices on file systems have been effective in uncovering hundreds of bugs, but they mostly focus on the sequential aspect of file system execution and do not comprehensively explore the concurrency dimension and hence, forgo the opportunity to catch data races.In this paper, we bring coverage-guided fuzzing to the concurrency dimension with three new constructs: 1) a new coverage tracking metric, alias coverage, specially designed to capture the exploration progress in the concurrency dimension; 2) an evolution algorithm for generating, mutating, and merging multi-threaded syscall sequences as inputs for concurrency fuzzing; and 3) a comprehensive lockset and happens-before modeling for kernel synchronization primitives for precise data race detection. These components are integrated into Krace, an end-to-end fuzzing framework that has discovered 23 data races in ext4, btrfs, and the VFS layer so far, and 9 are confirmed to be harmful.
{"title":"Krace: Data Race Fuzzing for Kernel File Systems","authors":"Meng Xu, Sanidhya Kashyap, Hanqing Zhao, Taesoo Kim","doi":"10.1109/SP40000.2020.00078","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00078","url":null,"abstract":"Data races occur when two threads fail to use proper synchronization when accessing shared data. In kernel file systems, which are highly concurrent by design, data races are common mistakes and often wreak havoc on the users, causing inconsistent states or data losses. Prior fuzzing practices on file systems have been effective in uncovering hundreds of bugs, but they mostly focus on the sequential aspect of file system execution and do not comprehensively explore the concurrency dimension and hence, forgo the opportunity to catch data races.In this paper, we bring coverage-guided fuzzing to the concurrency dimension with three new constructs: 1) a new coverage tracking metric, alias coverage, specially designed to capture the exploration progress in the concurrency dimension; 2) an evolution algorithm for generating, mutating, and merging multi-threaded syscall sequences as inputs for concurrency fuzzing; and 3) a comprehensive lockset and happens-before modeling for kernel synchronization primitives for precise data race detection. These components are integrated into Krace, an end-to-end fuzzing framework that has discovered 23 data races in ext4, btrfs, and the VFS layer so far, and 9 are confirmed to be harmful.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"26 1","pages":"1643-1660"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87925930","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00003
Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, E. Weippl
A reliable source of randomness is not only an essential building block in various cryptographic, security, and distributed systems protocols, but also plays an integral part in the design of many new blockchain proposals. Consequently, the topic of publicly-verifiable, bias-resistant and unpredictable randomness has recently enjoyed increased attention. In particular random beacon protocols, aimed at continuous operation, can be a vital component for current Proof-of-Stake based distributed ledger proposals. We improve upon previous random beacon approaches with HydRand, a novel distributed protocol based on publicly-verifiable secret sharing (PVSS) to ensure unpredictability, bias-resistance, and public-verifiability of a continuous sequence of random beacon values. Furthermore, HydRand provides guaranteed output delivery of randomness at regular and predictable intervals in the presence of adversarial behavior and does not rely on a trusted dealer for the initial setup. Compared to existing PVSS based approaches that strive to achieve similar properties, our solution improves scalability by lowering the communication complexity from $mathcal{O}left( {{n^3}} right)$ to $mathcal{O}left( {{n^2}} right)$ . Furthermore, we are the first to present a detailed comparison of recently described schemes and protocols that can be used for implementing random beacons.
{"title":"HydRand: Efficient Continuous Distributed Randomness","authors":"Philipp Schindler, Aljosha Judmayer, Nicholas Stifter, E. Weippl","doi":"10.1109/SP40000.2020.00003","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00003","url":null,"abstract":"A reliable source of randomness is not only an essential building block in various cryptographic, security, and distributed systems protocols, but also plays an integral part in the design of many new blockchain proposals. Consequently, the topic of publicly-verifiable, bias-resistant and unpredictable randomness has recently enjoyed increased attention. In particular random beacon protocols, aimed at continuous operation, can be a vital component for current Proof-of-Stake based distributed ledger proposals. We improve upon previous random beacon approaches with HydRand, a novel distributed protocol based on publicly-verifiable secret sharing (PVSS) to ensure unpredictability, bias-resistance, and public-verifiability of a continuous sequence of random beacon values. Furthermore, HydRand provides guaranteed output delivery of randomness at regular and predictable intervals in the presence of adversarial behavior and does not rely on a trusted dealer for the initial setup. Compared to existing PVSS based approaches that strive to achieve similar properties, our solution improves scalability by lowering the communication complexity from $mathcal{O}left( {{n^3}} right)$ to $mathcal{O}left( {{n^2}} right)$ . Furthermore, we are the first to present a detailed comparison of recently described schemes and protocols that can be used for implementing random beacons.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"714 1","pages":"73-89"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76906405","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00117
Cornelius Aschermann, Sergej Schumilo, A. Abbasi, Thorsten Holz
Although current fuzz testing (fuzzing) methods are highly effective, there are still many situations such as complex state machines where fully automated approaches fail. State-of-the-art fuzzing methods offer very limited ability for a human to interact and aid the fuzzer in such cases. More specifically, most current approaches are limited to adding a dictionary or new seed inputs to guide the fuzzer. When dealing with complex programs, these mechanisms are unable to uncover new parts of the code base.In this paper, we propose Ijon, an annotation mechanism that a human analyst can use to guide the fuzzer. In contrast to the two aforementioned techniques, this approach allows a more systematic exploration of the program’s behavior based on the data representing the internal state of the program. As a consequence, using only a small (usually one line) annotation, a user can help the fuzzer to solve previously unsolvable challenges. We extended various AFL-based fuzzers with the ability to annotate the source code of the target application with guidance hints. Our evaluation demonstrates that such simple annotations are able to solve problems that—to the best of our knowledge— no other current fuzzer or symbolic execution based tool can overcome. For example, with our extension, a fuzzer is able to play and solve games such as Super Mario Bros. or resolve more complex patterns such as hash map lookups. To further demonstrate the capabilities of our annotations, we use AFL combined with Ijon to uncover both novel security issues and issues that previously required a custom and comprehensive grammar to be uncovered. Lastly, we show that using Ijon and AFL, one can solve many challenges from the CGC data set that resisted all fully automated and human guided attempts so far.
{"title":"Ijon: Exploring Deep State Spaces via Fuzzing","authors":"Cornelius Aschermann, Sergej Schumilo, A. Abbasi, Thorsten Holz","doi":"10.1109/SP40000.2020.00117","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00117","url":null,"abstract":"Although current fuzz testing (fuzzing) methods are highly effective, there are still many situations such as complex state machines where fully automated approaches fail. State-of-the-art fuzzing methods offer very limited ability for a human to interact and aid the fuzzer in such cases. More specifically, most current approaches are limited to adding a dictionary or new seed inputs to guide the fuzzer. When dealing with complex programs, these mechanisms are unable to uncover new parts of the code base.In this paper, we propose Ijon, an annotation mechanism that a human analyst can use to guide the fuzzer. In contrast to the two aforementioned techniques, this approach allows a more systematic exploration of the program’s behavior based on the data representing the internal state of the program. As a consequence, using only a small (usually one line) annotation, a user can help the fuzzer to solve previously unsolvable challenges. We extended various AFL-based fuzzers with the ability to annotate the source code of the target application with guidance hints. Our evaluation demonstrates that such simple annotations are able to solve problems that—to the best of our knowledge— no other current fuzzer or symbolic execution based tool can overcome. For example, with our extension, a fuzzer is able to play and solve games such as Super Mario Bros. or resolve more complex patterns such as hash map lookups. To further demonstrate the capabilities of our annotations, we use AFL combined with Ijon to uncover both novel security issues and issues that previously required a custom and comprehensive grammar to be uncovered. Lastly, we show that using Ijon and AFL, one can solve many challenges from the CGC data set that resisted all fully automated and human guided attempts so far.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"255 1","pages":"1597-1612"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73123603","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00058
S. Ainsworth, Timothy M. Jones
Use-after-free vulnerabilities have plagued software written in low-level languages, such as C and C++, becoming one of the most frequent classes of exploited software bugs. Attackers identify code paths where data is manually freed by the programmer, but later incorrectly reused, and take advantage by reallocating the data to themselves. They then alter the data behind the program’s back, using the erroneous reuse to gain control of the application and, potentially, the system. While a variety of techniques have been developed to deal with these vulnerabilities, they often have unacceptably high performance or memory overheads, especially in the worst case.We have designed MarkUs, a memory allocator that prevents this form of attack at low overhead, sufficient for deployment in real software, even under allocation- and memory-intensive scenarios. We prevent use-after-free attacks by quarantining data freed by the programmer and forbidding its reallocation until we are sure that there are no dangling pointers targeting it. To identify these we traverse live-objects accessible from registers and memory, marking those we encounter, to check whether quarantined data is accessible from any currently allocated location. Unlike garbage collection, which is unsafe in C and C++, MarkUs ensures safety by only freeing data that is both quarantined by the programmer and has no identifiable dangling pointers. The information provided by the programmer’s allocations and frees further allows us to optimise the process by freeing physical addresses early for large objects, specialising analysis for small objects, and only performing marking when sufficient data is in quarantine. Using MarkUs, we reduce the overheads of temporal safety in low-level languages to 1.1× on average for SPEC CPU2006, with a maximum slowdown of only 2×, vastly improving upon the state-of-the-art.
{"title":"MarkUs: Drop-in use-after-free prevention for low-level languages","authors":"S. Ainsworth, Timothy M. Jones","doi":"10.1109/SP40000.2020.00058","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00058","url":null,"abstract":"Use-after-free vulnerabilities have plagued software written in low-level languages, such as C and C++, becoming one of the most frequent classes of exploited software bugs. Attackers identify code paths where data is manually freed by the programmer, but later incorrectly reused, and take advantage by reallocating the data to themselves. They then alter the data behind the program’s back, using the erroneous reuse to gain control of the application and, potentially, the system. While a variety of techniques have been developed to deal with these vulnerabilities, they often have unacceptably high performance or memory overheads, especially in the worst case.We have designed MarkUs, a memory allocator that prevents this form of attack at low overhead, sufficient for deployment in real software, even under allocation- and memory-intensive scenarios. We prevent use-after-free attacks by quarantining data freed by the programmer and forbidding its reallocation until we are sure that there are no dangling pointers targeting it. To identify these we traverse live-objects accessible from registers and memory, marking those we encounter, to check whether quarantined data is accessible from any currently allocated location. Unlike garbage collection, which is unsafe in C and C++, MarkUs ensures safety by only freeing data that is both quarantined by the programmer and has no identifiable dangling pointers. The information provided by the programmer’s allocations and frees further allows us to optimise the process by freeing physical addresses early for large objects, specialising analysis for small objects, and only performing marking when sufficient data is in quarantine. Using MarkUs, we reduce the overheads of temporal safety in low-level languages to 1.1× on average for SPEC CPU2006, with a maximum slowdown of only 2×, vastly improving upon the state-of-the-art.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"9 1","pages":"578-591"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76332558","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00050
Sean Bowe, A. Chiesa, M. Green, Ian Miers, Pratyush Mishra, Howard Wu
Ledger-based systems that support rich applications often suffer from two limitations. First, validating a transaction requires re-executing the state transition that it attests to. Second, transactions not only reveal which application had a state transition but also reveal the application’s internal state.We design, implement, and evaluate ZEXE, a ledger-based system where users can execute offline computations and subsequently produce transactions, attesting to the correctness of these computations, that satisfy two main properties. First, transactions hide all information about the offline computations. Second, transactions can be validated in constant time by anyone, regardless of the offline computation.The core of ZEXE is a construction for a new cryptographic primitive that we introduce, decentralized private computation (DPC) schemes. In order to achieve an efficient implementation of our construction, we leverage tools in the area of cryptographic proofs, including succinct zero knowledge proofs and recursive proof composition. Overall, transactions in ZEXE are 968 bytes regardless of the offline computation, and generating them takes less than 1min plus a time that grows with the offline computation.We demonstrate how to use ZEXE to realize privacy-preserving analogues of popular applications: private user-defined assets and private decentralized exchanges for these assets.
{"title":"ZEXE: Enabling Decentralized Private Computation","authors":"Sean Bowe, A. Chiesa, M. Green, Ian Miers, Pratyush Mishra, Howard Wu","doi":"10.1109/SP40000.2020.00050","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00050","url":null,"abstract":"Ledger-based systems that support rich applications often suffer from two limitations. First, validating a transaction requires re-executing the state transition that it attests to. Second, transactions not only reveal which application had a state transition but also reveal the application’s internal state.We design, implement, and evaluate ZEXE, a ledger-based system where users can execute offline computations and subsequently produce transactions, attesting to the correctness of these computations, that satisfy two main properties. First, transactions hide all information about the offline computations. Second, transactions can be validated in constant time by anyone, regardless of the offline computation.The core of ZEXE is a construction for a new cryptographic primitive that we introduce, decentralized private computation (DPC) schemes. In order to achieve an efficient implementation of our construction, we leverage tools in the area of cryptographic proofs, including succinct zero knowledge proofs and recursive proof composition. Overall, transactions in ZEXE are 968 bytes regardless of the offline computation, and generating them takes less than 1min plus a time that grows with the offline computation.We demonstrate how to use ZEXE to realize privacy-preserving analogues of popular applications: private user-defined assets and private decentralized exchanges for these assets.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"65 1","pages":"947-964"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78832771","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00049
Benedikt Bünz, Lucianna Kiffer, Loi Luu, M. Zamani
To validate transactions, cryptocurrencies such as Bitcoin and Ethereum require nodes to verify that a blockchain is valid. This entails downloading and verifying all blocks, taking hours and requiring gigabytes of bandwidth and storage. Hence, clients with limited resources cannot verify transactions independently without trusting full nodes. Bitcoin and Ethereum offer light clients known as simplified payment verification (SPV) clients, that can verify the chain by downloading only the block headers. Unfortunately, the storage and bandwidth requirements of SPV clients still increase linearly with the chain length. For example, as of July 2019, an SPV client in Ethereum needs to download and store about 4 GB of data.Recently, Kiayias et al. proposed a solution known as noninteractive proofs of proof-of-work (NIPoPoW) that allows a light client to download and store only a polylogarithmic number of block headers in expectation. Unfortunately, NIPoPoWs are succinct only as long as no adversary influences the honest chain, and can only be used in chains with fixed block difficulty, contrary to most cryptocurrencies which adjust block difficulty frequently according to the network hashrate.We introduce FlyClient, a novel transaction verification light client for chains of variable difficulty. FlyClient is efficient both asymptotically and practically and requires downloading only a logarithmic number of block headers while storing only a single block header between executions. Using an optimal probabilistic block sampling protocol and Merkle Mountain Range (MMR) commitments, FlyClient overcomes the limitations of NIPoPoWs and generates shorter proofs over all measured parameters. In Ethereum, FlyClient achieves a synchronization proof size of less than 500 KB which is roughly 6,600x smaller than SPV proofs. We finally discuss how FlyClient can be deployed with minimal changes to the existing cryptocurrencies via an uncontentious velvet fork.
为了验证交易，比特币和以太坊等加密货币需要节点验证区块链是否有效。这需要下载和验证所有块，需要几个小时，需要千兆字节的带宽和存储空间。因此，资源有限的客户端在不信任完整节点的情况下无法独立验证事务。比特币和以太坊提供被称为简化支付验证(SPV)客户端的轻客户端，它可以通过下载区块头来验证链。不幸的是，SPV客户端的存储和带宽需求仍然随着链长度线性增加。例如，截至2019年7月，以太坊的SPV客户端需要下载并存储约4gb的数据。最近，Kiayias等人提出了一种称为非交互式工作量证明(NIPoPoW)的解决方案，该解决方案允许轻客户端下载并存储预期的多对数数量的块头。不幸的是，NIPoPoWs只有在没有对手影响诚实链的情况下才能简洁，并且只能在固定块难度的链中使用，这与大多数根据网络哈希率频繁调整块难度的加密货币相反。介绍了一种新型的可变难度链交易验证轻客户端FlyClient。FlyClient在渐近和实际方面都是高效的，并且只需要下载对数数量的块头，而在执行之间只存储一个块头。使用最优概率块采样协议和Merkle Mountain Range (MMR)承诺，FlyClient克服了NIPoPoWs的限制，并对所有测量参数生成更短的证明。在以太坊中，FlyClient实现了小于500 KB的同步证明大小，大约比SPV证明小6600倍。我们最后讨论了如何通过无争议的天鹅绒分叉对现有加密货币进行最小的更改来部署FlyClient。
{"title":"FlyClient: Super-Light Clients for Cryptocurrencies","authors":"Benedikt Bünz, Lucianna Kiffer, Loi Luu, M. Zamani","doi":"10.1109/SP40000.2020.00049","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00049","url":null,"abstract":"To validate transactions, cryptocurrencies such as Bitcoin and Ethereum require nodes to verify that a blockchain is valid. This entails downloading and verifying all blocks, taking hours and requiring gigabytes of bandwidth and storage. Hence, clients with limited resources cannot verify transactions independently without trusting full nodes. Bitcoin and Ethereum offer light clients known as simplified payment verification (SPV) clients, that can verify the chain by downloading only the block headers. Unfortunately, the storage and bandwidth requirements of SPV clients still increase linearly with the chain length. For example, as of July 2019, an SPV client in Ethereum needs to download and store about 4 GB of data.Recently, Kiayias et al. proposed a solution known as noninteractive proofs of proof-of-work (NIPoPoW) that allows a light client to download and store only a polylogarithmic number of block headers in expectation. Unfortunately, NIPoPoWs are succinct only as long as no adversary influences the honest chain, and can only be used in chains with fixed block difficulty, contrary to most cryptocurrencies which adjust block difficulty frequently according to the network hashrate.We introduce FlyClient, a novel transaction verification light client for chains of variable difficulty. FlyClient is efficient both asymptotically and practically and requires downloading only a logarithmic number of block headers while storing only a single block header between executions. Using an optimal probabilistic block sampling protocol and Merkle Mountain Range (MMR) commitments, FlyClient overcomes the limitations of NIPoPoWs and generates shorter proofs over all measured parameters. In Ethereum, FlyClient achieves a synchronization proof size of less than 500 KB which is roughly 6,600x smaller than SPV proofs. We finally discuss how FlyClient can be deployed with minimal changes to the existing cryptocurrencies via an uncontentious velvet fork.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"51 1","pages":"928-946"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73876156","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00044
Ittai Abraham, D. Malkhi, Kartik Nayak, Ling Ren, Maofan Yin
Synchronous solutions for Byzantine Fault Tolerance (BFT) can tolerate up to minority faults. In this work, we present Sync HotStuff, a surprisingly simple and intuitive synchronous BFT solution that achieves consensus with a latency of 2Δ in the steady state (where Δ is a synchronous message delay upper bound). In addition, Sync HotStuff ensures safety in a weaker synchronous model in which the synchrony assumption does not have to hold for all replicas all the time. Moreover, Sync HotStuff has optimistic responsiveness, i.e., it advances at network speed when less than one-quarter of the replicas are not responding. Borrowing from practical partially synchronous BFT solutions, Sync HotStuff has a two-phase leader-based structure, and has been fully prototyped under the standard synchrony assumption. When tolerating a single fault, Sync HotStuff achieves a throughput of over 280 Kops/sec under typical network performance, which is comparable to the best known partially synchronous solution.
{"title":"Sync HotStuff: Simple and Practical Synchronous State Machine Replication","authors":"Ittai Abraham, D. Malkhi, Kartik Nayak, Ling Ren, Maofan Yin","doi":"10.1109/SP40000.2020.00044","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00044","url":null,"abstract":"Synchronous solutions for Byzantine Fault Tolerance (BFT) can tolerate up to minority faults. In this work, we present Sync HotStuff, a surprisingly simple and intuitive synchronous BFT solution that achieves consensus with a latency of 2Δ in the steady state (where Δ is a synchronous message delay upper bound). In addition, Sync HotStuff ensures safety in a weaker synchronous model in which the synchrony assumption does not have to hold for all replicas all the time. Moreover, Sync HotStuff has optimistic responsiveness, i.e., it advances at network speed when less than one-quarter of the replicas are not responding. Borrowing from practical partially synchronous BFT solutions, Sync HotStuff has a two-phase leader-based structure, and has been fully prototyped under the standard synchrony assumption. When tolerating a single fault, Sync HotStuff achieves a throughput of over 280 Kops/sec under typical network performance, which is comparable to the best known partially synchronous solution.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"20 1","pages":"106-118"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90150445","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00069
Kevin A. Roundy, Paula Barmaimon Mendelberg, Nicola Dell, Damon McCoy, Daniel N. Nissani, T. Ristenpart, Acar Tamersoy
Technology increasingly facilitates interpersonal attacks such as stalking, abuse, and other forms of harassment. While prior studies have examined the ecosystem of software designed for stalking, there exists an unstudied, larger landscape of apps—what we call creepware—used for interpersonal attacks. In this paper, we initiate a study of creepware using access to a dataset detailing the mobile apps installed on over 50 million Android devices. We develop a new algorithm, CreepRank, that uses the principle of guilt by association to help surface previously unknown examples of creepware, which we then characterize through a combination of quantitative and qualitative methods. We discovered apps used for harassment, impersonation, fraud, information theft, concealment, and even apps that purport to defend victims against such threats. As a result of our work, the Google Play Store has already removed hundreds of apps for policy violations. More broadly, our findings and techniques improve understanding of the creepware ecosystem, and will inform future efforts that aim to mitigate interpersonal attacks.
{"title":"The Many Kinds of Creepware Used for Interpersonal Attacks","authors":"Kevin A. Roundy, Paula Barmaimon Mendelberg, Nicola Dell, Damon McCoy, Daniel N. Nissani, T. Ristenpart, Acar Tamersoy","doi":"10.1109/SP40000.2020.00069","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00069","url":null,"abstract":"Technology increasingly facilitates interpersonal attacks such as stalking, abuse, and other forms of harassment. While prior studies have examined the ecosystem of software designed for stalking, there exists an unstudied, larger landscape of apps—what we call creepware—used for interpersonal attacks. In this paper, we initiate a study of creepware using access to a dataset detailing the mobile apps installed on over 50 million Android devices. We develop a new algorithm, CreepRank, that uses the principle of guilt by association to help surface previously unknown examples of creepware, which we then characterize through a combination of quantitative and qualitative methods. We discovered apps used for harassment, impersonation, fraud, information theft, concealment, and even apps that purport to defend victims against such threats. As a result of our work, the Google Play Store has already removed hundreds of apps for policy violations. More broadly, our findings and techniques improve understanding of the creepware ecosystem, and will inform future efforts that aim to mitigate interpersonal attacks.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"3 1","pages":"626-643"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87335357","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00030
Rui Zhang, C. Sturton
This paper presents Transys, a tool for translating security critical properties written for one hardware design to analogous properties suitable for a second design. Transys works in three passes adjusting the variable names, arithmetic expressions, logical preconditions, and timing constraints of the original property to retain the intended semantics of the property while making it valid for the second design. We evaluate Transys by translating 27 assertions written in a temporal logic and 9 properties written for use with gate level information flow tracking across 38 AES designs, 3 RSA designs, and 5 RISC processor designs. Transys successfully translates 96% of the properties. Among these, the translation of 23 (64%) of the properties achieved a semantic equivalence rate of above 60%. The average translation time per property is about 70 seconds.
{"title":"Transys: Leveraging Common Security Properties Across Hardware Designs","authors":"Rui Zhang, C. Sturton","doi":"10.1109/SP40000.2020.00030","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00030","url":null,"abstract":"This paper presents Transys, a tool for translating security critical properties written for one hardware design to analogous properties suitable for a second design. Transys works in three passes adjusting the variable names, arithmetic expressions, logical preconditions, and timing constraints of the original property to retain the intended semantics of the property while making it valid for the second design. We evaluate Transys by translating 27 assertions written in a temporal logic and 9 properties written for use with gate level information flow tracking across 38 AES designs, 3 RSA designs, and 5 RISC processor designs. Transys successfully translates 96% of the properties. Among these, the translation of 23 (64%) of the properties achieved a semantic equivalence rate of above 60%. The average translation time per property is about 70 seconds.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"37 1","pages":"1713-1727"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86566351","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 : 2020-05-01DOI: 10.1109/SP40000.2020.00015
Tao Wang
Traffic analysis attacks to identify which web page a client is browsing, using only her packet metadata — known as website fingerprinting (WF) — has been proven effective in closed-world experiments against privacy technologies like Tor. We want to investigate their usefulness in the real open world. Several WF attacks claim to have high recall and low false positive rate, but they have only been shown to succeed against high base rate pages. We explicitly incorporate the base rate into precision and call it r-precision. Using this metric, we show that the best previous attacks have poor precision when the base rate is realistically low; we study such a scenario (r = 1000), where the maximum r-precision achieved was only 0.14.To improve r-precision, we propose three novel classes of precision optimizers that can be applied to any classifier to increase precision. For r = 1000, our best optimized classifier can achieve a precision of at least 0.86, representing a precision increase by more than 6 times. For the first time, we show a WF classifier that can scale to any open world set size. We also investigate the use of precise classifiers to tackle realistic objectives in website fingerprinting, including different types of websites, identification of sensitive clients, and defeating website fingerprinting defenses.
{"title":"High Precision Open-World Website Fingerprinting","authors":"Tao Wang","doi":"10.1109/SP40000.2020.00015","DOIUrl":"https://doi.org/10.1109/SP40000.2020.00015","url":null,"abstract":"Traffic analysis attacks to identify which web page a client is browsing, using only her packet metadata — known as website fingerprinting (WF) — has been proven effective in closed-world experiments against privacy technologies like Tor. We want to investigate their usefulness in the real open world. Several WF attacks claim to have high recall and low false positive rate, but they have only been shown to succeed against high base rate pages. We explicitly incorporate the base rate into precision and call it r-precision. Using this metric, we show that the best previous attacks have poor precision when the base rate is realistically low; we study such a scenario (r = 1000), where the maximum r-precision achieved was only 0.14.To improve r-precision, we propose three novel classes of precision optimizers that can be applied to any classifier to increase precision. For r = 1000, our best optimized classifier can achieve a precision of at least 0.86, representing a precision increase by more than 6 times. For the first time, we show a WF classifier that can scale to any open world set size. We also investigate the use of precise classifiers to tackle realistic objectives in website fingerprinting, including different types of websites, identification of sensitive clients, and defeating website fingerprinting defenses.","PeriodicalId":6849,"journal":{"name":"2020 IEEE Symposium on Security and Privacy (SP)","volume":"85 4 1","pages":"152-167"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89341247","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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