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

共 556 条记录，以下是11-20 订阅

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Exploiting Update Leakage in Searchable Symmetric Encryption 24

Exploiting Update Leakage in Searchable Symmetric Encryption

引用

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

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

引用

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...

引用

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...

引用

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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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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Coherent Multi-Table data Synthesis for Tabular and Time-Series data with GANs 24

Coherent Multi-Table Data Synthesis for Tabular and Time-Ser...

引用

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

作者： Elliker, Clement Tonnelier, Emeric Shabou, Aymen Grp Credit Agricole DataLab Grp Paris France

ISBN: (纸本)9798400704215

As the usage of user-private-data is increasingly monitored by regulatory institutions for security purposes, its transfer becomes more constrained. Synthetic data has recently emerged as a viable alternative to prevent the disclosure of user-protected information that complies with data sharing regulations. Both public and private sectors commonly use a combination of tabular and time-series tables that often contains user-related sensitive information. They are usually intrinsically interlinked as they describe the users and their behaviors over different perimeters. Moreover, it contains both numerical and categorical features, adding complexity to the anonymization task. State of the art generative methods, specialized either in tabular or time-series data, are able to generate high quality synthetic data. However, if each table is generated independently, it becomes impossible to link them. As a result, the usability of such synthetic data is impacted. To address this issue, we not only propose a coherent multi-table generative model that uses Generative Adversarial Networks (GANs) to sample both tabular and time-series tables, but also a conditional time-series generative model that handles both numerical and categorical features. Additionally, many experiments are conducted to analyse the inner modules of our model and evaluate it on an in-house private dataset in order to prove the viability of the synthetic data generated for machine learning tasks.

关键词： synthetic data generative ai gan tabular data time-series data deep learning data privacy

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Efficient privacy-Preserving Approximation of the Kidney Exchange Problem 19

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

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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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A Preliminary Study on the privacy Concerns of Using IP Addresses in Log data 32

A Preliminary Study on the Privacy Concerns of Using IP Addr...

引用

32nd acm International conference on the Foundations of Software Engineering (FSE)

作者： Sedki, Issam Concordia Univ Montreal PQ Canada

ISBN: (纸本)9798400706585

Log data, crucial for system monitoring and debugging, inherently contains information that may conflict with privacy safeguards. This study addresses the delicate interplay between log utility and the protection of sensitive data, with a focus on how IP addresses are recorded. We scrutinize the logging practices against the privacy policies of Linux, OpenSSH, and MacOS, uncovering discrepancies that hint at broader privacy concerns. Our methodology, anchored in privacy benchmarks like GDPR, evaluates both open-source and commercial systems, revealing that the former may lack rigorous privacy controls. The research finds that the actual logging of IP addresses often deviates from policy statements, especially in open-source systems. By systematically contrasting stated policies with practical application, our study identifies privacy risks and advocates for policy reform.

关键词： Software Logging Software Maintenance and Evolution data privacy and security Regulatory Compliance

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CoCoT: Collaborative Contact Tracing 24

CoCoT: Collaborative Contact Tracing

引用

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

作者： Kann, Trevor Bauer, Lujo Cunningham, Robert K. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9798400704215

Contact tracing can limit the spread of infectious diseases by notifying people of potential exposure to disease. Manual contact tracing is resource-intensive, but much of it can be automated using mobile phones, which are ubiquitous and can detect and record nearby contacts. Two major problems arise with automated contact tracing (ACT): preventing abuse for mass surveillance and accurately determining contacts. For example, the most widely adopted solution-Google and Apple's Exposure Notification (GAEN)-protects user privacy but suffers from inaccurate distance measurements that result in poor risk assessments. We propose to use collaboration among nearby devices to increase distance estimation accuracy, and therefore risk assessment accuracy, while minimizing the loss of user privacy. Our protocol, CoCoT, extends GAEN, a proximity-based, distributed ACT protocol, by adding an additional broadcast to share locally-derived distance estimates. To evaluate CoCoT, we develop a method for merging phone sensor datasets with human interaction datasets to approximate realistic scenarios and test our protocol. CoCoT improves distance estimate accuracy by 28% over the current best distance estimators and we analytically show impact on privacy, security, and battery consumption are minimal.

关键词： Automatic Contact Tracing Distributed Optimization Network Localization Smartphone privacy

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Crypto'Graph: Leveraging privacy-Preserving Distributed Link Prediction for Robust Graph Learning 24

Crypto'Graph: Leveraging Privacy-Preserving Distributed Link...

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

作者： Azogagh, Sofiane Birba, Zelma Aubin Gambs, Sebastien Killijian, Marc-Olivier Univ Quebec Montreal Montreal PQ Canada

ISBN: (纸本)9798400704215

Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still be willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. Through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private graph of each party, even though they know the number of nodes they have, since they share the same graph in terms of nodes but not the same links. Crypto'Graph improves on previous works by enabling the computation of a diverse set of similarity metrics in parallel without any additional cost. The use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which potential adversarial links are identified without compromising the privacy of the graphs of individual parties. The effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a node classification task using graph neural networks is demonstrated through extensive experimentation on two real-world datasets.

关键词： security privacy secure multiparty computation graph

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