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检索条件"任意字段=14th ACM Conference on Data and Application Security and Privacy, CODASPY 2024"

共 54 条记录，以下是1-10 订阅

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codaspy 2024 - Proceedings of the 14th acm conference on data and application security and privacy

CODASPY 2024 - Proceedings of the 14th ACM Conference on Dat...

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14th acm conference on data and application security and privacy, codaspy 2024

ISBN: (纸本)9798400704215

the proceedings contain 43 papers. the topics discussed include: estimating the runtime and global network traffic of SMPC protocols;a unified time series analytics based intrusion detection framework for CAN BUS attacks;investigating TLS version downgrade in enterprise software;examining cryptography and randomness failures in open-source cellular cores;nothing personal: understanding the spread and use of personally identifiable information in the financial ecosystem;CCSM: building cross-cluster security models for edge-core environments involving multiple Kubernetes clusters;process-aware intrusion detection in MQTT networks;accelerating performance of bilinear map cryptography using FPGA;exploiting update leakage in searchable symmetric encryption;and the avg-act swap and plaintext overflow detection in fully homomorphic operations over deep circuits.

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SLIM-View: Sampling and Private Publishing of Multidimensional databases 24

SLIM-View: Sampling and Private Publishing of Multidimension...

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

作者： Laouir, Ala Eddine Imine, Abdessamad Univ Lorraine CNRS Nancy France INRIA Nancy France

ISBN: (纸本)9798400704215

Despite the enormous data processing capacity available in big data frameworks, obtaining appropriate and private responses to large-scale queries without revealing sensitive information is still a challenging problem. In this paper, we address the problem of combining offline sampling techniques for space efficiency in multidimensional databases and Differential privacy (DP) to protect sensitive data. We present our framework SLIM-View, which uses a novel sampling technique relying on a bi-objective optimization to decide the best sample size and the exponential mechanism to select the best sample while ensuring privacy. Our extensive experiments demonstrate that SLIM-View outperforms existing approaches by orders of magnitude in terms of utility and scalability while ensuring the same level of privacy.

关键词： Multidimensional data privacy Differential privacy data sampling

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A-COPILOT: Android Covert Operation for Private Information Lifting and OTP theft 24

A-COPILOT: Android Covert Operation for Private Information ...

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

作者： Lim, Joseph Guan Quan Kwok, Zhen Yu Soon, Isaac Yong, Jun Xian Yuhao, Samuel Song Rosley, Siti Halilah Binte Balachandran, Vivek Singapore Inst Technol Singapore Singapore

ISBN: (纸本)9798400704215

this paper investigates an underexplored avenue of cybersecurity threats in mobile computing, with a particular focus on the Android platform which, due to its open nature and widespread adoption, is a fertile ground for cyber threats. We present a Proof-of-Concept (PoC) Android application that, while ostensibly benign, can execute covert malicious operations by exploiting the process of permission granted. Specifically, the app manipulates accessibility permissions to autonomously acquire additional permissions needed for executing unauthorized activities without the user's knowledge. Our work outlines conditions for minimal detection risk, leveraging periods when users are less likely to interact with their devices. the study provides a deeper understanding of the abuse potential of Android's accessibility features and highlights the critical need for comprehensive security measures to counteract such exploitations.

关键词： Android platform Permission exploitation Accessibility services

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From theory to Comprehension: A Comparative Study of Differential privacy and κ-Anonymity 24

From Theory to Comprehension: A Comparative Study of Differe...

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

作者： von Voigt, Saskia Nunez Mehner, Luise Tschorsch, Florian Tech Univ Berlin Berlin Germany Tech Univ Dresden Dresden Germany

ISBN: (纸本)9798400704215

the notion of epsilon-differential privacy is a widely used concept of providing quantifiable privacy to individuals. However, it is unclear how to explain the level of privacy protection provided by a differential privacy mechanism with a set epsilon. In this study, we focus on users' comprehension of the privacy protection provided by a differential privacy mechanism. To do so, we study three variants of explaining the privacy protection provided by differential privacy: (1) the original mathematical definition;(2) epsilon translated into a specific privacy risk;and (3) an explanation using the randomized response technique. We compare users' comprehension of privacy protection employing these explanatory models with their comprehension of privacy protection of kappa-anonymity as baseline comprehensibility. Our findings suggest that participants' comprehension of differential privacy protection is enhanced by the privacy risk model and the randomized response-based model. Moreover, our results confirm our intuition that privacy protection provided by :-anonymity is more comprehensible.

关键词： differential privacy explanatory model study

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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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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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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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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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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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IoTFuzzSentry: Hunting Bugs In the IoT Wilderness In Operational Phase Using Payload Fuzzing 24

IoTFuzzSentry: Hunting Bugs In The IoT Wilderness In Operati...

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

作者： Chaudhary, Priyanka Rushikesh Krishna, N. Pranav Maiti, Rajib Ranjan BITS Pilani Hyderabad Campus Secunderabad India

ISBN: (纸本)9798400704215

In the operational phase, an IoT device runs a light-weight server that is responsible for responding to the user queries, like accessing video and taking a snap in a IoT camera. the flaws in the implementation of certain security mechanisms in these IoT devices can lead to varying level of security threats. In this paper, we address the problems of discovering such vulnerabilities using effective fuzzing via crafted transport and upper layer protocol packets in IoT communication. We have designed and developed a mutation-based fuzzing tool, named IoTFuzzSentry, to discover certain non-trivial vulnerabilities in commercial IoT devices and demonstrate their potential exploits. We have used IoT devices, like IP cameras and Smart Plug. We have categorized the vulnerabilities into four types (IoT Credential Leakage (V1), Sneak IP camera live video stream (V2), Creep Live IoT Image (V3), IoT Command Injection (V4)) and show their exploits using three IoT devices. We have reported all these vulnerabilities to respective vendors via email. We believe that our IoTFuzzSentry has a great potential to discover such unconventional security threats and allow IoT vendors to strengthen the security of their commercialized devices.

关键词： Mutation Fuzzing IoT Devices Vulnerability privacy

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