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CrashTalk: Automated Generation of Precise, Human Readable, Descriptions of Software security Bugs 24

CrashTalk: Automated Generation of Precise, Human Readable, ...

引用

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

作者： James, Kedrian Valakuzhy, Kevin Snow, Kevin Monrose, Fabian Univ N Carolina Chapel Hill NC 27515 USA Georgia Inst Technol Atlanta GA 30332 USA Zeropoint Dynam Chapel Hill NC USA

ISBN: (纸本)9798400704215

Understanding the cause, consequences, and severity of a security bug are critical facets of the overall bug triaging and remediation process. Unfortunately, diagnosing failures is often a laborious process that requires developers to expend significant time and effort. While solutions have been proposed to help expedite the process of pinpointing the cause of a security bug, few proposals provide an explanation along with a diagnosis to make the bug discovery and triaging process less taxing. Moreover, even in cases where descriptions are provided, they are not guided by classification models that support precise descriptions of the flaw. We present an approach that uses static and dynamic analysis techniques to automatically infer the cause and consequences of a software crash and present diagnostic information following NIST's recently released Bugs Framework taxonomy. Specifically, starting from a crash, we generate a detailed and accessible English description of the failure along with its weakness types and severity, thereby easing the burden on developers and security analysts alike. To evaluate the effectiveness of our approach, we compare our ability to find fault locations and generate explanations compared to that of professional software developers by using a benchmark specifically designed to assist with realistic evaluation of tools in software engineering. In addition, using 33 real-world vulnerabilities we collected, we show that our approach correctly diagnoses over 94% of the failures and, in some cases, generates weakness types that are more specific than those that were originally assigned by the submitter or National Vulnerability database analysts. We also generate initial vulnerability scores that can be used by project managers to assist with prioritizing bug fixes. On average, the overall process takes just over a minute, which is orders of magnitude faster than what professional developers can do.

关键词： Bug Localization Debugging Vulnerabilities

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Velocity-Aware Geo-Indistinguishability 23

Velocity-Aware Geo-Indistinguishability

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13th acm conference on data and application security and privacy (CODASPY)

作者： Mendes, Ricardo Cunha, Mariana Vilela, Joao P. Univ Coimbra CISUC Coimbra Portugal Univ Coimbra Dept Informat Coimbra Portugal Univ Porto Fac Ciencias CISUC CRACS INESCTEC Rua Campo Alegre S-N P-4169007 Porto Portugal Univ Porto Fac Ciencias Dept Ciencia Comp Rua Campo Alegre S-N P-4169007 Porto Portugal

ISBN: (纸本)9798400700675

Location privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. This adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. This approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

关键词： Location privacy Differential privacy Adaptive privacy privacy Budget Location-Based Services

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Risky Cohabitation: Understanding and Addressing Over-privilege Risks of Commodity application Virtualization Platforms in Android 24

Risky Cohabitation: Understanding and Addressing Over-privil...

引用

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

作者： Hsiao, Shou-Ching Li, Shih-Wei Hsiao, Hsu-Chun Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan

ISBN: (纸本)9798400704215

The Android system protects its users' privacy via app permissions, which govern apps' access to sensitive data and resources. However, recent research has reported that, during app virtualization, the current Android permission model fails to prevent illegal permission usage: apps can exploit the User ID shared among co-hosted apps in the same virtualized environment to perform unauthorized actions. To the best of our knowledge, such over-privilege issues have not been thoroughly investigated;neither has a practical defense proposed to address them. To fill those gaps, this paper introduces a taxonomy of over-privilege issues in the context of app virtualization and presents an automated tool called PermLabel to investigate their prevalence and impact in real-world apps. By testing 826 real-world apps, PermLabel identified 254 (31%) as having over-privilege issues. Among the found apps, 244 (96%) have at least one over-privileged runtime permission, which poses a substantial risk to user privacy. Our analysis of these identified cases uncovered common causes and abuses of over-privileged permissions. Notably, the majority of such permissions grant access to sensitive data such as location, camera, and phone state. These issues predominantly stem from the app's code and third-party libraries intended for advertising and analytics. To restore compliance with the Android permission model, we also propose a defense solution, PermSep, that enforces fine-grained permission separation among co-hosted apps while preserving normal app-virtualization usage. Evaluation of PermSep on 17 virtualization apps shows successful blocking of over-privilege, achieving desired properties not attainable by previous work.

关键词： Over-privilege App Virtualization Android Permissions

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Derecho: privacy Pools with Proof-Carrying Disclosures 24

Derecho: Privacy Pools with Proof-Carrying Disclosures

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31st conference on Computer and Communications security

作者： Beal, Josh Ben Fisch Yale Univ New Haven CT 06511 USA

ISBN: (纸本)9798400706363

A privacy pool enables clients to deposit units of a cryptocurrency into a shared pool where ownership of deposited currency is tracked via a system of cryptographically hidden records. Clients may later withdraw from the pool without linkage to previous deposits. Some privacy pools also support hidden transfer of currency ownership within the pool. In August 2022, the U.S. Department of Treasury sanctioned Tornado Cash, the largest Ethereum privacy pool, on the premise that it enables illicit actors to hide the origin of funds, citing its usage by the DPRK-sponsored Lazarus Group to launder over $455 million dollars worth of stolen cryptocurrency. This ruling effectively made it illegal for U.S. persons/institutions to use or accept funds that went through Tornado Cash, sparking a global debate among privacy rights activists and lawmakers. Against this backdrop, we present Derecho, a system that institutions could use to request cryptographic attestations of fund origins rather than naively rejecting all funds coming from privacy pools. Derecho is a novel application of proof-carrying data, which allows users to propagate allowlist membership proofs through a privacy pool's transaction graph. Derecho is backwards-compatible with existing Ethereum privacy pool designs, adds no overhead in gas costs, and costs users only a few seconds to produce attestations.

关键词： Proof-Carrying data Smart Contracts Cryptocurrencies privacy Pools Zero-Knowledge Proofs

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privacy-Preserving Fully Online Matching with Deadlines 23

Privacy-Preserving Fully Online Matching with Deadlines

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13th acm conference on data and application security and privacy (CODASPY)

作者： Klinger, Andreas Meyer, Ulrike Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798400700675

In classical secure multi-party computation (SMPC) it is assumed that a fixed and a priori known set of parties wants to securely evaluate a function of their private inputs. This assumption implies that online problems, in which the set of parties that arrive and leave over time are not a priori known, are not covered by the classical setting. Therefore, the notion of online SMPC has been introduced, and a general feasibility result has been proven that shows that any online algorithm can be implemented as a distributed protocol that is secure in this setting [22, 23]. However, so far, no online SMPC protocol that implements a concrete online algorithm has been proposed and evaluated such that the practicality of the constructive proof is an open question. We close this gap and propose the first privacy-preserving online SMPC protocol for the prominent problem of fully online matching with deadlines. In this problem an (a priori unknown) set of parties with their inputs arrive over time and can then be matched with other parties until they leave when their individual deadline is reached. We prove that our protocol is statistically secure in the presence of a semi-honest adversary that controls strictly less than half of the parties present at each point in time. We extensively evaluate the performance of our protocol in three different network settings, various input sizes and different matching conditions, as well as various numbers of parties.

关键词： SMPC online matching privacy

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Re-pseudonymization Strategies for Smart Meter data Are Not Robust to Deep Learning Profiling Attacks 24

Re-pseudonymization Strategies for Smart Meter Data Are Not ...

引用

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

作者： Cretu, Ana-Maria Rusu, Miruna de Montjoye, Yves-Alexandre Ecole Polytech Fed Lausanne Lausanne Switzerland Imperial Coll London London England

ISBN: (纸本)9798400704215

Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extremely useful, including in the fight against climate change. However, these data and the information that can be inferred from them are highly sensitive. Re-pseudonymization, i.e., the frequent replacement of random identifiers over time, is widely used to share smart meter data while mitigating the risk of re-identification. We here show how, in spite of re-pseudonymization, households' consumption records can be pieced together with high accuracy in large-scale datasets. More specifically, we propose the first deep learning-based profiling attack against re-pseudonymized smart meter data. Our attack combines neural network embeddings, which are used to extract features from weekly consumption records and are tailored to the smart meter identification task, with a nearest neighbor classifier. We evaluate six neural networks architectures as the embedding model. Our results suggest that the Transformer and CNN-LSTM architectures vastly outperform previous methods as well as other architectures, successfully identifying the correct household 73.4% of the time among 5139 households based on electricity and gas consumption records (54.5% for electricity only). We further show that the features extracted by the embedding model maintain their effectiveness when transferred to a set of users disjoint from the one used to train the model. Finally, we extensively evaluate the robustness of our results. In particular, we show that the accuracy of the attack only slowly decreases with the size of the dataset, how less frequent re-pseudonymization will further increase accuracy, and how an attacker can evaluate the likelihood of a match to be correct. Taken together, our results strongly suggest that even frequent re-pseudonymization strategies can be reversed, strongly limiting their ability to prev

关键词： Smart meter data Re-pseudonymization Profiling attacks Re-identification attacks

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Swipe2Pair: Secure and Fast In-Band Wireless Device Pairing 24

Swipe2Pair: Secure and Fast In-Band Wireless Device Pairing

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17th acm conference on security and privacy in Wireless and Mobile Networks (WiSec)

作者： He, Yaqi Zeng, Kai Jiao, Long Mark, Brian L. Khasawneh, Khaled N. George Mason Univ Wireless Cyber Ctr Fairfax VA 22030 USA Univ Massachusetts Dartmouth Dept Comp Informat Sci Dartmouth NS Canada

ISBN: (纸本)9798400705823

Wireless device pairing is a critical security mechanism to bootstrap the secure communication between two devices without a pre-shared secret. It has been widely used in many Internet of Things (IoT) applications, such as smart-home and smart-health. Most existing device pairing mechanisms are based on out-of-band channels, e.g., extra sensors or hardware, to validate the proximity of pairing devices. However, out-of-band channels are not universal across all wireless devices, so such a scheme is limited to certain application scenarios or conditions. On the other hand, in-band channel-based device pairing seeks universal applicability by only relying on wireless interfaces. Existing in-band channel-based pairing schemes either require multiple antennas separated by a good distance on one pairing device, which is not feasible in certain scenarios, or require users to repeat multiple sweeps, which is not optimal in terms of usability. Therefore, an in-band wireless device pairing scheme providing high security while maintaining high usability (simple pairing process and minimal user intervention) is highly desired. In this work, we propose an easy-to-use mutual authentication device pairing scheme, named Swipe2Pair, based on the proximity of pairing devices and randomization of wireless transmission power. We conduct extensive security analysis and collect considerable experimental data under various settings across different environments. Experimental results show that Swipe2Pair achieves high security and usability. It only takes less than one second to complete the pairing process with a simple swipe of one device in front of the other.

关键词： security Protocol Authentication In-band Device Pairing Wireless Communication System

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Anonymous System for Fully Distributed and Robust Secure Multi-Party Computation 23

Anonymous System for Fully Distributed and Robust Secure Mul...

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13th acm conference on data and application security and privacy (CODASPY)

作者： Klinger, Andreas Battermann, Felix Meyer, Ulrike Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798400700675

In secure multi-party computation (SMPC), it is considered that multiple parties that are known to each other evaluate a function over their private inputs in a secure fashion. The participating parties do not learn anything about each other's private inputs beyond what can be deduced from their own input and output. The assumption that the parties know each other, however, does not seem suitable for all potential applications of SMPC. In some applications participants may not only want to hide their private inputs and outputs, but may also want to hide the fact that they are participating in a given function evaluation in the first place. We therefore propose an anonymous system for SMPC that allows parties to anonymously evaluate a function of their private inputs in a fully distributed and secure fashion. The proposed system allows authorized parties to execute an SMPC protocol robust with penalty against a dishonest majority in the presence of a malicious adversary. During the protocol execution, the system guarantees that all participating parties stay anonymous w.r.t. each other as well as any third parties. In addition, it guarantees that in each function evaluation all participating parties are unique, i.e., no party can participate as more than one entity.

关键词： SMPC anonymity privacy

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Federated Learning for privacy Preserving Intelligent Healthcare application to Breast Cancer Detection 25

Federated Learning for Privacy Preserving Intelligent Health...

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26th International conference on Distributed Computing and Networking, ICDCN 2025

作者： Amritanjali Gupta, Ragav Birla Institute of Technology Mesra Ranchi Ranchi India Sri Mata Vaishno Devi University Jammu Katra India

ISBN: (纸本)9798400710629

Health data used in building intelligent medical care systems contain sensitive personal information of the patients. Distributed storage, processing and sharing of data poses threats to the data privacy and security. Federated learning (FL) is an emerging approach that allows multiple entities to collaboratively train a model without sharing their data, thus preserving data privacy. This paper explores the implementation of federated learning techniques for deep learning-based breast cancer detection using the publicly available BRCA dataset. The efficacy of both centralized and federated approaches has been examined through various performance metrics. This work is an effort to reduce the gap between privacy-preserving techniques and practical healthcare applications, offering insights into the trade-offs between data privacy and model performance. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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Comparative privacy Analysis of Mobile Browsers 23

Comparative Privacy Analysis of Mobile Browsers

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13th acm conference on data and application security and privacy (CODASPY)

作者： Zafar, Ahsan Das, Anupam North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9798400700675

Online trackers are invasive as they track our digital footprints, many of which are sensitive in nature, and when aggregated over time, they can help infer intricate details about our lifestyles and habits. Although much research has been conducted to understand the effectiveness of existing countermeasures for the desktop platform, little is known about how mobile browsers have evolved to handle online trackers. With mobile devices now generating more web traffic than their desktop counterparts, we fill this research gap through a large-scale comparative analysis of mobile web browsers. We crawl 10K valid websites from the Tranco list on real mobile devices. Our data collection process covers both popular generic browsers (e.g., Chrome, Firefox, and Safari) as well as privacy-focused browsers (e.g., Brave, Duck Duck Go, and Firefox-Focus). We use dynamic analysis of runtime execution traces and static analysis of source codes to highlight the tracking behavior of invasive fingerprinters. We also find evidence of tailored content being served to different browsers. In particular, we note that Firefox Focus sees altered script code, whereas Brave and Duck Duck Go have highly similar content. To test the privacy protection of browsers, we measure the responses of each browser in blocking trackers and advertisers and note the strengths and weaknesses of privacy browsers. To establish ground truth, we use well-known block lists, including EasyList, Easyprivacy, Disconnect and WhoTracksMe and find that Brave generally blocks the highest number of content that should be blocked as per these lists. Focus performs better against social trackers, and Duck Duck Go restricts third-party trackers that perform email-based tracking.

关键词： Web tracking User privacy Mobile browsers

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