Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3237221
M. Song, Zhongyun Hua, Yifeng Zheng, Hejiao Huang, Xiaohua Jia
Cloud computing promises great advantages in handling the exponential data growth. Secure deduplication can greatly improve cloud storage efficiency while protecting data confidentiality. In the meantime, when data are outsourced to the remote cloud, there is an imperative need to audit the integrity. Most existing works only consider the support for either secure deduplication or integrity auditing. Recently, there have been some research efforts aiming to integrate secure deduplication with integrity auditing. However, prior works are unsatisfactory in that they suffer from the leakage of ownership privacy and forgeability of auditing results for low-entropy data. In this paper, we propose a new scheme that delicately bridges secure deduplication and integrity auditing in encrypted cloud storage. In contrast with prior works, our scheme protects the ownership privacy and prevents the cloud service provider from forging the auditing results for low-entropy data. Furthermore, we propose a blockchain-based mechanism that helps to ensure key recoverability and reduce local storage cost of keys. Formal analysis is provided to justify the security guarantees. Experiment results demonstrate the modest performance overhead of our scheme.
{"title":"Blockchain-Based Deduplication and Integrity Auditing over Encrypted Cloud Storage","authors":"M. Song, Zhongyun Hua, Yifeng Zheng, Hejiao Huang, Xiaohua Jia","doi":"10.1109/tdsc.2023.3237221","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3237221","url":null,"abstract":"Cloud computing promises great advantages in handling the exponential data growth. Secure deduplication can greatly improve cloud storage efficiency while protecting data confidentiality. In the meantime, when data are outsourced to the remote cloud, there is an imperative need to audit the integrity. Most existing works only consider the support for either secure deduplication or integrity auditing. Recently, there have been some research efforts aiming to integrate secure deduplication with integrity auditing. However, prior works are unsatisfactory in that they suffer from the leakage of ownership privacy and forgeability of auditing results for low-entropy data. In this paper, we propose a new scheme that delicately bridges secure deduplication and integrity auditing in encrypted cloud storage. In contrast with prior works, our scheme protects the ownership privacy and prevents the cloud service provider from forging the auditing results for low-entropy data. Furthermore, we propose a blockchain-based mechanism that helps to ensure key recoverability and reduce local storage cost of keys. Formal analysis is provided to justify the security guarantees. Experiment results demonstrate the modest performance overhead of our scheme.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62409938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3240820
Lili Wang, Ye Lin, Ting Yao, H. Xiong, K. Liang
Existing proxy re-encryption (PRE) schemes to secure cloud data sharing raise challenges such as supporting the heterogeneous system efficiently and achieving the unbounded feature. To address this problem, we proposed a fast and secure unbounded cross-domain proxy re-encryption scheme, named FABRIC, which enables the delegator to authorize the semi-trusted cloud server to convert one ciphertext of an identity-based encryption (IBE) scheme to another ciphertext of an attribute-based encryption (ABE) scheme. As the first scheme to achieve the feature mentioned above, FABRIC not only enjoys constant computation overhead in the encryption, decryption, and re-encryption phases when the quantity of attributes increases, but is also unbounded such that the new attributes or roles could be adopted into the system anytime. Furthermore, FABRIC achieves adaptive security under the decisional linear assumption (DLIN). Eventually, detailed theoretical and experimental analysis proved that FABRIC enjoys excellent performance in efficiency and practicality in the cloud computing scenario.
{"title":"FABRIC: Fast and Secure Unbounded Cross-System Encrypted Data Sharing in Cloud Computing","authors":"Lili Wang, Ye Lin, Ting Yao, H. Xiong, K. Liang","doi":"10.1109/tdsc.2023.3240820","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3240820","url":null,"abstract":"Existing proxy re-encryption (PRE) schemes to secure cloud data sharing raise challenges such as supporting the heterogeneous system efficiently and achieving the unbounded feature. To address this problem, we proposed a fast and secure unbounded cross-domain proxy re-encryption scheme, named FABRIC, which enables the delegator to authorize the semi-trusted cloud server to convert one ciphertext of an identity-based encryption (IBE) scheme to another ciphertext of an attribute-based encryption (ABE) scheme. As the first scheme to achieve the feature mentioned above, FABRIC not only enjoys constant computation overhead in the encryption, decryption, and re-encryption phases when the quantity of attributes increases, but is also unbounded such that the new attributes or roles could be adopted into the system anytime. Furthermore, FABRIC achieves adaptive security under the decisional linear assumption (DLIN). Eventually, detailed theoretical and experimental analysis proved that FABRIC enjoys excellent performance in efficiency and practicality in the cloud computing scenario.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3238332
Zhong-Liang Ge, Yi Zhang, Yu Long, Dawu Gu
The lack of scalability is a leading issue of blockchain. By transferring transactions to off-chain, 2-party payment channels achieve instant transaction confirmation between channel users and enhance the blockchain throughput, thereby becoming a promising solution. By extending the channel from 2-party to multi-party, richer application scenarios could be supported. Meanwhile, new and exclusive requirements emerge in the multi-party off-chain payments, including robustness and flexibility. The robustness requires that the channel operation would not be impeded by any uncooperative channel member, and the flexibility guarantees that parties could join or exit the channel dynamically. However, all the current attempts either fail to achieve the new emerging properties or sacrifice some merits of 2-party channels. In this paper, we propose a new multi-party channel construction, Magma, which has good scalability. Magma outperforms the previous solutions to the multi-party payment channel for the following reasons. By canceling the heavy reliance on the cooperation of all channel members when implementing the channel operation, Magma achieves robustness. Magma also allows parties to join or exit one channel flexibly, without violating the balance security. Meanwhile, Magma's whole transaction process is performed off-chain, thereby inheriting the instant confirmation and low-cost features of 2-party channels. To guarantee the security of Magma, we formalize the multi-party channel's functionality and prove that Magma is secure in the UC framework. Moreover, our implementation and comparison show that Magma is practical and performs better than existing solutions in providing off-chain payment services.
{"title":"Magma: Robust and Flexible Multi-Party Payment Channel","authors":"Zhong-Liang Ge, Yi Zhang, Yu Long, Dawu Gu","doi":"10.1109/tdsc.2023.3238332","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3238332","url":null,"abstract":"The lack of scalability is a leading issue of blockchain. By transferring transactions to off-chain, 2-party payment channels achieve instant transaction confirmation between channel users and enhance the blockchain throughput, thereby becoming a promising solution. By extending the channel from 2-party to multi-party, richer application scenarios could be supported. Meanwhile, new and exclusive requirements emerge in the multi-party off-chain payments, including robustness and flexibility. The robustness requires that the channel operation would not be impeded by any uncooperative channel member, and the flexibility guarantees that parties could join or exit the channel dynamically. However, all the current attempts either fail to achieve the new emerging properties or sacrifice some merits of 2-party channels. In this paper, we propose a new multi-party channel construction, Magma, which has good scalability. Magma outperforms the previous solutions to the multi-party payment channel for the following reasons. By canceling the heavy reliance on the cooperation of all channel members when implementing the channel operation, Magma achieves robustness. Magma also allows parties to join or exit one channel flexibly, without violating the balance security. Meanwhile, Magma's whole transaction process is performed off-chain, thereby inheriting the instant confirmation and low-cost features of 2-party channels. To guarantee the security of Magma, we formalize the multi-party channel's functionality and prove that Magma is secure in the UC framework. Moreover, our implementation and comparison show that Magma is practical and performs better than existing solutions in providing off-chain payment services.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3238547
S. Mallissery, Kun-Yi Chiang, Chun-An Bau, Yu-Sung Wu
Detection of advanced security attacks that exploit zero-day vulnerabilities or application-specific logic loopholes has been challenging due to the lack of attack signatures or substantial deviations in the overall system behavior. One has to zoom in to the affected code regions and look for local anomalies distinguishable from the benign workload to detect such attacks. We propose pervasive micro information flow tracking (PerMIT) that realizes variable-level online dynamic information flow tracking (DIFT) as a means to detect the attacks. The system uses hardware virtualization extension to monitor access to taint source variables and performs asynchronous code emulation to infer the local information flow. We demonstrate that the pervasive micro information flow can sufficiently capture the attacks and incurs only a small overhead. Given the program source code, the system can further enrich the semantics of micro information flow by embedding the variable names. We have integrated the system with machine learning algorithms to demonstrate the effectiveness of anomaly detection for zero-day attacks with pervasive micro information flow.
{"title":"Pervasive Micro Information Flow Tracking","authors":"S. Mallissery, Kun-Yi Chiang, Chun-An Bau, Yu-Sung Wu","doi":"10.1109/tdsc.2023.3238547","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3238547","url":null,"abstract":"Detection of advanced security attacks that exploit zero-day vulnerabilities or application-specific logic loopholes has been challenging due to the lack of attack signatures or substantial deviations in the overall system behavior. One has to zoom in to the affected code regions and look for local anomalies distinguishable from the benign workload to detect such attacks. We propose pervasive micro information flow tracking (PerMIT) that realizes variable-level online dynamic information flow tracking (DIFT) as a means to detect the attacks. The system uses hardware virtualization extension to monitor access to taint source variables and performs asynchronous code emulation to infer the local information flow. We demonstrate that the pervasive micro information flow can sufficiently capture the attacks and incurs only a small overhead. Given the program source code, the system can further enrich the semantics of micro information flow by embedding the variable names. We have integrated the system with machine learning algorithms to demonstrate the effectiveness of anomaly detection for zero-day attacks with pervasive micro information flow.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2023.3241057
Xi Lin, Jun Wu, Jianhua Li, Chao Sang, Shiyan Hu, M. Deen
Differential-private federated learning (DP-FL) has emerged to prevent privacy leakage when disclosing encoded sensitive information in model parameters. However, the existing DP-FL frameworks usually preserve privacy homogeneously across clients, while ignoring the different privacy attitudes and expectations. Meanwhile, DP-FL is hard to guarantee that uncontrollable clients (i.e., stragglers) have truthfully added the expected DP noise. To tackle these challenges, we propose a heterogeneous differential-private federated learning framework, named HDP-FL, which captures the variation of privacy attitudes with truthful incentives. First, we investigate the impact of the HDP noise on the theoretical convergence of FL, showing a tradeoff between privacy loss and learning performance. Then, based on the privacy-utility tradeoff, we design a contract-based incentive mechanism, which encourages clients to truthfully reveal private attitudes and contribute to learning as desired. In particular, clients are classified into different privacy preference types and the optimal privacy-price contracts in the discrete-privacy-type model and continuous-privacy-type model are derived. Our extensive experiments with real datasets demonstrate that HDP-FL can maintain satisfactory learning performance while considering different privacy attitudes, which also validate the truthfulness, individual rationality, and effectiveness of our incentives.
{"title":"Heterogeneous Differential-Private Federated Learning: Trading Privacy for Utility Truthfully","authors":"Xi Lin, Jun Wu, Jianhua Li, Chao Sang, Shiyan Hu, M. Deen","doi":"10.1109/tdsc.2023.3241057","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3241057","url":null,"abstract":"Differential-private federated learning (DP-FL) has emerged to prevent privacy leakage when disclosing encoded sensitive information in model parameters. However, the existing DP-FL frameworks usually preserve privacy homogeneously across clients, while ignoring the different privacy attitudes and expectations. Meanwhile, DP-FL is hard to guarantee that uncontrollable clients (i.e., stragglers) have truthfully added the expected DP noise. To tackle these challenges, we propose a heterogeneous differential-private federated learning framework, named HDP-FL, which captures the variation of privacy attitudes with truthful incentives. First, we investigate the impact of the HDP noise on the theoretical convergence of FL, showing a tradeoff between privacy loss and learning performance. Then, based on the privacy-utility tradeoff, we design a contract-based incentive mechanism, which encourages clients to truthfully reveal private attitudes and contribute to learning as desired. In particular, clients are classified into different privacy preference types and the optimal privacy-price contracts in the discrete-privacy-type model and continuous-privacy-type model are derived. Our extensive experiments with real datasets demonstrate that HDP-FL can maintain satisfactory learning performance while considering different privacy attitudes, which also validate the truthfulness, individual rationality, and effectiveness of our incentives.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this article, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.
{"title":"Anchor Link Prediction for Privacy Leakage via De-Anonymization in Multiple Social Networks","authors":"Huanran Wang, Wu Yang, Dapeng Man, Wei Wang, Jiguang Lv","doi":"10.1109/tdsc.2023.3242009","DOIUrl":"https://doi.org/10.1109/tdsc.2023.3242009","url":null,"abstract":"Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this article, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62410727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2022.3228797
Guojun Chu, Jingyu Wang, Q. Qi, Haifeng Sun, Shimin Tao, Hao Yang, J. Liao, Zhu Han
Fraud detection in telecom network is a crucial problem that threatens users’ privacy and property security. In recent years, fraudsters adopt more advanced camouflage strategies to avoid being detected by traditional algorithms. To deal with these new types of fraud, it is necessary to analyze the integrated spatial-temporal features, which are rarely involved in existing literature. In this article, we propose a novel fraud detection model based on the intertwined spatial-temporal patterns of user behaviors. Specifically, we first introduce the extension of statistical and interactive features to dynamic call patterns, and build a probabilistic model to simulate users’ call behaviors. Then the sequential patterns reflecting users’ own behaviors are obtained by the mixture Hidden Markov Models, and the structural patterns reflecting the collaboration between users in the telecom network are obtained by the attention-based Graph-SAGE model. Finally, our model outputs a fraud score for each user to detect potential fraudsters. We conduct extensive experiments on a real-world telecom dataset. The experimental results demonstrate that our intertwined spatial-temporal call patterns can effectively represent user behavior and improve the accuracy of fraud detection compared with state-of-the-art methods. The results also validate the efficiency and the interpretability of our model.
{"title":"Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom Networks","authors":"Guojun Chu, Jingyu Wang, Q. Qi, Haifeng Sun, Shimin Tao, Hao Yang, J. Liao, Zhu Han","doi":"10.1109/tdsc.2022.3228797","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3228797","url":null,"abstract":"Fraud detection in telecom network is a crucial problem that threatens users’ privacy and property security. In recent years, fraudsters adopt more advanced camouflage strategies to avoid being detected by traditional algorithms. To deal with these new types of fraud, it is necessary to analyze the integrated spatial-temporal features, which are rarely involved in existing literature. In this article, we propose a novel fraud detection model based on the intertwined spatial-temporal patterns of user behaviors. Specifically, we first introduce the extension of statistical and interactive features to dynamic call patterns, and build a probabilistic model to simulate users’ call behaviors. Then the sequential patterns reflecting users’ own behaviors are obtained by the mixture Hidden Markov Models, and the structural patterns reflecting the collaboration between users in the telecom network are obtained by the attention-based Graph-SAGE model. Finally, our model outputs a fraud score for each user to detect potential fraudsters. We conduct extensive experiments on a real-world telecom dataset. The experimental results demonstrate that our intertwined spatial-temporal call patterns can effectively represent user behavior and improve the accuracy of fraud detection compared with state-of-the-art methods. The results also validate the efficiency and the interpretability of our model.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62407225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For vacating-room-after-encryption reversible data hiding in encrypted images (VRAE RDHEI), an essential problem is how to address potential errors in data extraction and image recovery. This problem significantly limits the capacities of the existing VRAE RDHEI methods. To solve the problem while losing as little capacity as possible, in this article, a novel method is proposed that uses the ideas of noisy channel model and channel code. By designing the data hiding mechanism appropriately, the embedding and extraction of data in the proposed method can be equivalent to the input and output of a virtual binary symmetric channel (BSC) model, so that the errors in data extraction are equivalent to the bit transitions in BSC. Based on the virtual BSC model, polar code is used to encode the secret data in the data hider's side. With the help of polar code, the receiver can decode the extracted bits containing errors to obtain correct secret data, then recover the error-free original image based on the corrected secret data. The experimental results proved that, compared with the existing VRAE methods, the proposed method can significantly improve the capacity and the quality of the decrypted images under the premise of complete reversibility.
{"title":"Reversible Data Hiding in Encrypted Images Based on Binary Symmetric Channel Model and Polar Code","authors":"Kaimeng Chen, Qingxiao Guan, Weiming Zhang, Nenghai Yu","doi":"10.1109/tdsc.2022.3228385","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3228385","url":null,"abstract":"For vacating-room-after-encryption reversible data hiding in encrypted images (VRAE RDHEI), an essential problem is how to address potential errors in data extraction and image recovery. This problem significantly limits the capacities of the existing VRAE RDHEI methods. To solve the problem while losing as little capacity as possible, in this article, a novel method is proposed that uses the ideas of noisy channel model and channel code. By designing the data hiding mechanism appropriately, the embedding and extraction of data in the proposed method can be equivalent to the input and output of a virtual binary symmetric channel (BSC) model, so that the errors in data extraction are equivalent to the bit transitions in BSC. Based on the virtual BSC model, polar code is used to encode the secret data in the data hider's side. With the help of polar code, the receiver can decode the extracted bits containing errors to obtain correct secret data, then recover the error-free original image based on the corrected secret data. The experimental results proved that, compared with the existing VRAE methods, the proposed method can significantly improve the capacity and the quality of the decrypted images under the premise of complete reversibility.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62407423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2022.3231237
Jiajie Zhang, Bingsheng Zhang, A. Nastenko, Hamed Balogun, R. Oliynykov
Many blockchain applications require democratic on-chain decision-making. In this work, we propose a community-inclusive decentralised collaborative decision-making system with privacy assurance. Its key component is a two-stage voting scheme inspired by choice architecture. Our decision-making system is compatible with most existing blockchain infrastructures. In addition, it supports liquid democracy/delegative voting for better collaborative intelligence. Namely, stake holders can either vote directly on proposals or delegate their voting power to experts. When majority of voting committee members are honest, no one can derive voters’ voting preferences or delegations with non-negligible probability. To support concurrent multiple voting events, we design a distributed batch key generation protocol that can generate multiple keys simultaneously by voting committee members with amortised communication cost of $mathcal {O}(n)$O(n) per key, where $n$n is the number of participants. Besides, our system supports “evolving committee”, i.e., voting committee members can be changed during the voting period. We implemented a pilot system in Scala, benchmark results indicate that our system can support large number of participants with high efficiency.
{"title":"Privacy-Preserving Decision-Making over Blockchain","authors":"Jiajie Zhang, Bingsheng Zhang, A. Nastenko, Hamed Balogun, R. Oliynykov","doi":"10.1109/tdsc.2022.3231237","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3231237","url":null,"abstract":"Many blockchain applications require democratic on-chain decision-making. In this work, we propose a community-inclusive decentralised collaborative decision-making system with privacy assurance. Its key component is a two-stage voting scheme inspired by choice architecture. Our decision-making system is compatible with most existing blockchain infrastructures. In addition, it supports liquid democracy/delegative voting for better collaborative intelligence. Namely, stake holders can either vote directly on proposals or delegate their voting power to experts. When majority of voting committee members are honest, no one can derive voters’ voting preferences or delegations with non-negligible probability. To support concurrent multiple voting events, we design a distributed batch key generation protocol that can generate multiple keys simultaneously by voting committee members with amortised communication cost of <inline-formula><tex-math notation=\"LaTeX\">$mathcal {O}(n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">O</mml:mi><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"zhang-ieq1-3231237.gif\"/></alternatives></inline-formula> per key, where <inline-formula><tex-math notation=\"LaTeX\">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href=\"zhang-ieq2-3231237.gif\"/></alternatives></inline-formula> is the number of participants. Besides, our system supports “evolving committee”, i.e., voting committee members can be changed during the voting period. We implemented a pilot system in Scala, benchmark results indicate that our system can support large number of participants with high efficiency.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62407495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/tdsc.2022.3230748
Kai Lehniger, P. Langendorfer
This paper presents Window Canaries, a novel approach to Stack Canaries for architectures with a register window that protects return addresses and stack pointers without the need of adding additional instruction to each potentially vulnerable function. Instead, placement and check of the canary word is moved to window exception handlers that are responsible to handle register window overflows and underflows. The approach offers low performance overhead while guaranteeing that return addresses are protected by stack buffer overflows without relying on a heuristic that decides which functions to instrument. The contributions of this paper are a complete implementation of the approach for the Xtensa LX architecture with register window option as well as a performance evaluation and discussion of advantages and drawbacks.
{"title":"Window Canaries: Re-thinking Stack Canaries for Architectures with Register Windows","authors":"Kai Lehniger, P. Langendorfer","doi":"10.1109/tdsc.2022.3230748","DOIUrl":"https://doi.org/10.1109/tdsc.2022.3230748","url":null,"abstract":"This paper presents Window Canaries, a novel approach to Stack Canaries for architectures with a register window that protects return addresses and stack pointers without the need of adding additional instruction to each potentially vulnerable function. Instead, placement and check of the canary word is moved to window exception handlers that are responsible to handle register window overflows and underflows. The approach offers low performance overhead while guaranteeing that return addresses are protected by stack buffer overflows without relying on a heuristic that decides which functions to instrument. The contributions of this paper are a complete implementation of the approach for the Xtensa LX architecture with register window option as well as a performance evaluation and discussion of advantages and drawbacks.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62407602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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