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检索条件"任意字段=Proceedings of the 5th ACM Conference on Data and Application Security and Privacy"

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CCSM: Building Cross-Cluster security Models for Edge-Core Environments Involving Multiple Kubernetes Clusters 24

CCSM: Building Cross-Cluster Security Models for Edge-Core E...

引用

14th acm conference on data and application security and privacy (acm CODASPY)

作者： GholipourChoubeh, Mahmood Kermabon-Bobinnec, Hugo Majumdar, Suryadipta Jarraya, Yosr Wang, Lingyu Nour, Boubakr Pourzandi, Makan Concordia Univ Montreal PQ Canada Ericsson Secur Res Montreal PQ Canada

ISBN: (纸本)9798400704215

With the emergence of 5G networks and their large scale applications such as IoT and autonomous vehicles, telecom operators are increasingly offloading the computation closer to customers (i.e., on the edge). Such edge-core environments usually involve multiple Kubernetes clusters potentially owned by different providers. Confidentiality concerns could prevent those providers from sharing data freely with each other, which makes it challenging to perform common security tasks such as security verification across different clusters. In this work, we propose a solution for building cross-cluster security models to enable various security analyses, while preserving confidentiality for each cluster. We design a six-step methodology to model both the cross-cluster communication and cross-cluster event dependency, and we apply those models to different security use cases. We implement our solution based on a 5G edge-core environment that involves multiple Kubernetes clusters, and our experimental results demonstrate its efficiency (e.g., less than 8 seconds of processing time for a model with 3,600 edges and nodes) and accuracy (e.g., more than 96% for cross-cluster event prediction).

关键词： Cloud Computing security Kubernetes 5G security Model

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Efficient privacy-Preserving Approximation of the Kidney Exchange Problem 19

Efficient Privacy-Preserving Approximation of the Kidney Exc...

引用

19th acm Asia conference on Computer and Communications security (acm AsiaCCS)

作者： Breuer, Malte Meyer, Ulrike Wetzel, Susanne Rhein Westfal TH Aachen Aachen Germany Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9798400704826

the kidney exchange problem (KEP) seeks to find possible exchanges among pairs of patients and their incompatible kidney donors while meeting specific optimization criteria such as maximizing the overall number of possible transplants. Recently, several privacy-preserving protocols for solving the KEP have been proposed. However, the protocols known to date lack scalability in practice since the KEP is an NP-complete problem. We address this issue by proposing a novel privacy-preserving protocol which computes an approximate solution for the KEP that scales well for the large numbers of patient-donor pairs encountered in practice. As opposed to prior work on privacy-preserving kidney exchange, our protocol is generic w.r.t. the security model that can be employed. Compared to the most efficient privacy-preserving protocols for kidney exchange existing to date, our protocol is entirely data oblivious and it exhibits a far superior run time performance. As a second contribution, we use a real-world data set to simulate the application of our protocol as part of a kidney exchange platform, where patient-donor pairs register and de-register over time, and thereby determine its approximation quality in a real-world setting.

关键词： Kidney Exchange Secure Multi-Party Computation privacy

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Examining Cryptography and Randomness Failures in Open-Source Cellular Cores 24

Examining Cryptography and Randomness Failures in Open-Sourc...

引用

14th acm conference on data and application security and privacy (acm CODASPY)

作者： English, K. Virgil Bennett, Nathaniel thorn, Seaver Butler, Kevin R. B. Enck, William Traynor, Patrick North Carolina State Univ Raleigh NC 27695 USA Univ Florida Gainesville FL USA

ISBN: (纸本)9798400704215

Industry is increasingly adopting private 5G networks to securely manage their wireless devices in retail, manufacturing, natural resources, and healthcare. As with most technology sectors, open-source software is well poised to form the foundation of deployments, whether it is deployed directly or as part of well-maintained proprietary offerings. this paper seeks to examine the use of cryptography and secure randomness in open-source cellular cores. We design a set of 13 CodeQL static program analysis rules for cores written in both C/C++ and Go and apply them to 7 open-source cellular cores implementing 4G and 5G functionality. We identify two significant security vulnerabilities, including predictable generation of TMSIs and improper verification of TLS certificates, with each vulnerability affecting multiple cores. In identifying these flaws, we hope to correct implementations to fix downstream deployments and derivative proprietary projects.

关键词： cellular core security cryptography misuse static analysis

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privacy Preserving Biometric Authentication for Fingerprints and Beyond 24

Privacy Preserving Biometric Authentication for Fingerprints...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Blanton, Marina Murphy, Dennis Univ Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9798400704215

Biometric authentication eliminates the need for users to remember secrets and serves as a convenient mechanism for user authentication. Traditional implementations of biometric-based authentication store sensitive user biometry on the server and the server becomes an attractive target of attack and a source of large-scale unintended disclosure of biometric data. To mitigate the problem, we can resort to privacy-preserving computation and store only protected biometrics on the server. While a variety of secure computation techniques is available, our analysis of privacy-preserving biometric authentication constructions revealed that available solutions fall short of addressing the challenges of privacy-preserving biometric authentication. thus, in this work we put forward new constructions to address the challenges. Our solutions employ a helper server and use strong threat models, where a client is always assumed to be malicious, while the helper server can be semi-honest or malicious. We also determined that standard secure multi-party computation definitions are insufficient to properly demonstrate security in the two-phase (enrollment and authentication) entity authentication application. We thus extend the model and formally show security in the multi-phase setting, where information can flow from one phase to another and the set of participants can change between the phases. We implement our constructions and show that they exhibit practical performance for authentication in real time.

关键词： secure computation biometric authentication multi-phase secure execution garbled circuit evaluation oblivious transfer

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Exploiting Update Leakage in Searchable Symmetric Encryption 24

Exploiting Update Leakage in Searchable Symmetric Encryption

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Haltiwanger, Jacob Hoang, thang Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9798400704215

Dynamic Searchable Symmetric Encryption (DSSE) provides efficient techniques for securely searching and updating an encrypted database. However, efficient DSSE schemes leak some sensitive information to the server. Recent works have implemented forward and backward privacy as security properties to reduce the amount of information leaked during update operations. Many attacks have shown that leakage from search operations can be abused to compromise the privacy of client queries. However, the attack literature has not rigorously investigated techniques to abuse update leakage. In this work, we investigate update leakage under DSSE schemes with forward and backward privacy from the perspective of a passive adversary. We propose two attacks based on a maximum likelihood estimation approach, the UFID Attack and the UF Attack, which target forward-private DSSE schemes with no backward privacy and Level II backward privacy, respectively. these are the first attacks to show that it is possible to leverage the frequency and contents of updates to recover client queries. We propose a variant of each attack which allows the update leakage to be combined with search pattern leakage to achieve higher accuracy. We evaluate our attacks against a real-world dataset and show that using update leakage can improve the accuracy of attacks against DSSE schemes, especially those without backward privacy.

关键词： Searchable Encryption Inference Analysis database privacy

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Comprehensive Review for Big data-Based Sensitive Information Secure Sharing Using Blockchain Techniques 5

Comprehensive Review for Big Data-Based Sensitive Informatio...

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5th International conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Murukanantham, K. Uma Nandhini, D. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Big data (BD) has attracted much attention from science and industry. Recent developments in BD have raised significant security issues and increased the risk of BD data privacy breaches. the novel reviews recent research and advancements in sensitive data security and privacy. this review explores various blockchain services for BD, including blockchain for securing significant data acquisition, data storage, analytics, and privacy protection. It then discusses recent approaches to Blockchain based privacy preservation techniques and reviews consensus algorithms and various other cryptographic methods like Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Elliptic Curve Cryptography (ECC) and Enhanced privacy-Preserving data Perturbation Algorithm (EPPDPA). Finally, discuss the accomplishments and drawbacks of recent methods, as well as highlight investigation open issues associated to the incorporation of blockchain into BD. © 2024 IEEE.

关键词： Encryption algorithms

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Communicate Less, Learn More: A Locally Differential Private Approach for Counting Triangles with Better Accuracy 24

Communicate Less, Learn More: A Locally Differential Private...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Saladi, Sasi Bhushan V. Sristy, Nagesh Bhattu Natl Inst Technol Comp Sci & Engn Tadepalligudem Andhra Pradesh India

ISBN: (纸本)9798400704215

Local differential privacy (LDP) offers strong privacy guarantees, but with reduced accuracy due to noise addition. More often than not, the accuracy of the statistical computations is improved at the cost of high communication overhead. the existing works either have less communication overhead at the cost of accuracy or have a better accuracy at the expense of huge communication cost or involve a trusted entity called shuffler and make an assumption that the shuffler and the data collector do not collude with each other. this work proposes a novel LDP mechanism, LocalShuffle, that addresses above challenges, improving communication efficiency while maintaining or even enhancing the accuracy of statistical analysis. Inspired by previous works, our approach leverages a single round algorithm to significantly reduce the amount of data communicated between devices and the central aggregator. Additionally, we incorporate novel noise injection technique, to minimize information loss and achieve higher accuracy in counting triangles. Our comprehensive evaluation demonstrates that the proposed mechanism achieves communication cost reduction, while preserving or even exceeding the accuracy compared to existing methods.

关键词： Local Differential privacy Shuffle model Asymmetric Randomized Response Sampling

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WikiPhish: A Diverse Wikipedia-Based dataset for Phishing Website Detection 24

WikiPhish: A Diverse Wikipedia-Based Dataset for Phishing We...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Loiseau, Gabriel Lefils, Valentin Meyer, Maxime Riquet, Damien Hornet Secur Hem France

ISBN: (纸本)9798400704215

Phishing remains a pervasive security threat, necessitating effective and universally comparable detection systems. the use of supervised machine learning models for phishing detection has been generalized in the literature to automate predictions and increase the detection capacities of security systems. these models rely on large amounts of annotated data for their training, evaluation and maintenance. thus, there is a need to efficiently collect significant amount of annotated data to improve phishing detection. this paper introduces WikiPhish, a novel, renewable, and open-access dataset for phishing website classification. It consists of 110,606 webpages harvested from URLs drawn from Wikipedia's references and the popular phishing databases OpenPhish and PhishTank. the dataset is designed to address the challenges of phishing detection by leveraging Wikipedia's contribution verification and wide-ranging content. WikiPhish offers a more diverse and robust baseline for developing phishing detection models. We highlight the importance of gathering diverse URLs for building phishing website datasets, and demonstrate the practical utility of WikiPhish by employing it in the training and evaluation of phishing detection machine learning models.

关键词： Web security Phishing Website Detection datasets Machine Learning

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Stop Stealing My data: Sanitizing Stego Channels in 3D Printing Design Files 24

Stop Stealing My Data: Sanitizing Stego Channels in 3D Print...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Dolgavin, Aleksandr Yampolskiy, Mark Yung, Moti Auburn Univ Auburn AL 36849 USA Google LLC Mountain View CA USA Columbia Univ New York NY USA

ISBN: (纸本)9798400704215

the increased adoption of additive manufacturing (AM) and the acceptance of AM outsourcing created an ecosystem in which the sending and receiving of digital designs by different actors became normal. It has recently been shown that the STL design files-most commonly used in AM-contain steganographic channels. Such channels can allow additional data to be embedded within the STL files without changing the printed model. these factors create a threat of misusing the design files as a covert communication channel to either exfiltrate stolen sensitive digital data from organizations or infiltrate malicious software into a secure environment. this paper addresses this security threat by designing and evaluating a sanitizer that erases hidden content where steganographic channels might exist. the proposed sanitizer takes into account a set of specific constraints imposed by the application domain, such as not affecting the ability to manufacture part of the required quality using the sanitized design.

关键词： Additive Manufacturing 3D Printing STL Design File Sanitizing.

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the Avg-Act Swap and Plaintext Overflow Detection in Fully Homomorphic Operations Over Deep Circuits 24

The Avg-Act Swap and Plaintext Overflow Detection in Fully H...

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14th acm conference on data and application security and privacy (acm CODASPY)

作者： Nam, Ihyun Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798400704215

Fully homomorphic encryption is a cryptographic scheme that enables any function to be computed on encrypted data. Although homomorphic evaluation on deep circuits has many real-life applications, fully homomorphic encryption is not commercialized due to its low speed and huge computational overhead. In the aim to make fully homomorphic operations faster and bridge the gap between security and practicality, we introduce the Avg-Act Swap. the Avg-Act Swap is a deployable tool in privacy-preserving machine learning;it places the average pool layer before the activation layer as opposed to the conventional practice of ordering them the other way around in neural networks over unencrypted data. We introduce two FHE-friendly convolutional neural networks and a modified version of Lenet-5 [26] that utilize the Avg-Act Swap to demonstrate improvements in encrypted inference speed. Most notably, we improve the encrypted inference speed of Lenet-5 by 28.58% after modifying it with the Avg-Act Swap, with a 90% accuracy. Plaintext overflow is a plausible problem in deep circuit homomorphic evaluations. We introduce (to our knowledge) the first formalized protocol to detect plaintext overflows in fixed-point arithmetic fully homomorphic encryption schemes that maintains indistinguishability over chosen plaintext attacks. We show that a remote server can homomorphically compute the maximum relative error bound of the client's plaintext only using encrypted inputs from the client. After all operations are done, the client can compare the received relative error bound to the actual error bound in the decrypted plaintext to detect an overflow. Further research to make this work in progress more efficient is encouraged.

关键词： Fully Homomorphic Encryption privacy Preserving Techniques Machine Learning Cheon-Kim-Kim-Song

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