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

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Honest-Majority Maliciously Secure Skyline Queries on Outsourced data 24

Honest-Majority Maliciously Secure Skyline Queries on Outsou...

引用

33rd acm International conference on Information and Knowledge Management (CIKM)

作者： Chen, Yu Liu, Lin Chen, Rongmao Fu, Shaojing Yang, Yuexiang Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798400704369

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the Honest-Majority and Maliciously Skyline Query scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.

关键词： secure skyline query skyline region splitting honest majority

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Poster: Longitudinal Measurement of the Adoption Dynamics in Apple's privacy Label Ecosystem 23

Poster: Longitudinal Measurement of the Adoption Dynamics in...

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30th acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Balash, David G. Ali, Mir Masood Kodwani, Monica Wu, Xiaoyuan Kanich, Chris Aviv, Adam J. Univ Richmond Richmond VA 23173 USA Univ Illinois Chicago IL 60680 USA George Washington Univ Washington DC 20052 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798400700507

This work reports on a large scale, longitudinal analysis of the adoption dynamics of privacy labels in the iOS App Store, measuring this first-of-its kind ecosystem as it reaches maturity over two and a half years after launching in December 2020. The motivation is to shed light on the factors affecting the shifts in privacy labels and provide insights into how and when an app's label changes. By collecting nearly weekly snapshots of over 1.6 million apps for over a year, we analyze the dynamics of privacy label adoption and the accuracy of reported labels. Our analysis of 74.5% of apps having labels after two years provides important context into this mature ecosystem where labels are becoming the standard. However, we find compelling evidence that labels may not fully capture behavior, as 28.9% of apps indicate no data collection and distributions differ between voluntary versus mandatory adoptions. Once set, labels rarely change but additions reflect more data collection. In addition to our measurement, we also plan to release a new (and growing) data set that can be used by future researchers.

关键词： privacy Labels iOS application App Marketplaces privacy

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MiniChecker: Detecting data privacy Risk of Abusive Permission Request Behavior in Mini-Programs 24

MiniChecker: Detecting Data Privacy Risk of Abusive Permissi...

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39th acm/IEEE International conference on Automated Software Engineering (ASE)

作者： Wang, Yin Fan, Ming Zhou, Hao Wang, Haijun Jin, Wuxia Li, Jiajia Chen, Wenbo Li, Shijie Zhang, Yu Han, Deqiang Liu, Ting Xi An Jiao Tong Univ Xian Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The rising popularity of mini-programs deployed on super-app platforms has drawn significant attention due to their convenience. However, developers' improper handling of data permission application in mini-programs has raised concerns about non-compliance and violations. Unfortunately, existing tools lack the capability to support the construction of a universal function call graph for the mini-program and the literature lacks a comprehensive and systematic study of the abusive issues. To bridge this gap, this paper introduces an automated tool, MiniChecker, to uncover the abusive permission request behavior in mini-programs. It defines five primary categories of abusive issues, namely homepage pop-up, overlaying pop-up, bothering pop-up, repeating pop-up, and looping pop-up, based on the request behavior features. MiniChecker achieves a detection precision rate of 82.4% and a recall rate of 95.3% on our benchmark, and identifies 3,866 risky mini-programs out of 20,000 real-world mini-programs. Our analysis reveals inherent design flaws in the mini-program permission mechanism, and we have shared our findings with several mini-program platforms.

关键词： Mini-programs data permission privacy protection mobile security

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Tool/dataset Paper: Realistic ABAC data Generation using Conditional Tabular GAN 23

Tool/Dataset Paper: Realistic ABAC Data Generation using Con...

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

作者： Rai, Ritwik Sural, Shamik Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400700675

Attribute-based Access Control (ABAC) is increasingly being used in a wide variety of applications that include cloud services, IoT, smart homes, healthcare and several others. Conducting systematic and reproducible experiments with benchmark realistic datasets, however, still remains a challenge. To address this shortcoming, in this paper we introduce a method called ConGRASS (Conditional Tabular GAN for Realistic ABAC Simulation Studies) for generating large ABAC datasets. Starting with a given real world dataset of (potentially) limited size, we first train a conditional tabular generative adversarial network for learning its distribution. The trained model is used to generate realistic datasets of arbitrarily large sizes having distribution similar to the original dataset. ConGRASS has been implemented as a free to use web-based tool in which a user can choose the name of a listed real dataset along with the desired dataset size. A CSV file containing ABAC data is generated as output. Extensive evaluation shows the ability of the model to faithfully learn the statistical properties of the selected real data. When such a dataset is used in an actual problem, significant improvement in performance is achieved, proving the utility of ConGRASS.

关键词： Attribute-based Access Control (ABAC) Conditional GAN Tabular data generation ABAC simulation studies

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Laboratory Management and Computer Network security Maintenance Strategies in the Era of Artificial Intelligence 24

Laboratory Management and Computer Network Security Maintena...

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5th International conference on Computer Science and Management Technology, ICCSMT 2024

作者： Lai, Zhenhuan Wu, Qixing School of Mechanics and Construction Engineering Jinan University Guangdong Guangzhou China

ISBN: (纸本)9798400709999

In the era of artificial intelligence, the integration of AI technology into network security has transformed how threats are detected and *** paper explores the application of AI in maintaining network security, with a specific focus on laboratory management *** enhances real-time monitoring, automates intrusion detection, and facilitates dynamic defense strategies in both general networks and laboratory *** management systems, which handle sensitive data and complex equipment networks, particularly benefit from AI-driven security *** study outlines how AI can improve the efficiency of security systems while minimizing human *** also discusses the challenges related to resource consumption, false positives, and privacy concerns in both network security and laboratory *** directions for AI development in network security are proposed, with an emphasis on optimizing algorithms and balancing security needs with resource management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to acm.

关键词： Resource allocation

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I Don't Know Why You Need My data: A Case Study of Popular Social Media privacy Policies 22

I Don't Know Why You Need My Data: A Case Study of Popular S...

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

作者： Miller, Elizabeth Rahman, Md Rashedur Hossain, Moinul Ali-Gombe, Aisha Towson Univ Towson MD 21252 USA

ISBN: (纸本)9781450392204

data privacy, a critical human right, is gaining importance as new technologies are developed, and the old ones evolve. In mobile platforms such as Android, data privacy regulations require developers to communicate data access requests using privacy policy statements (PPS). This case study cross-examines the privacy policy in popular social media (SM) apps - Facebook and Twitter - constituting 314 candidate statements for features of language ambiguity, sensitive data requests, and whether the statements tally with the data requests made in the Manifest file. Subsequently, we conduct a comparative analysis between the PPS of these two apps to examine trends that may constitute a threat to user data privacy.

关键词： privacy Policy Facebook Twitter Social Media privacy Policy Analysis

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A New Bound for privacy Loss from Bayesian Posterior Sampling 22

A New Bound for Privacy Loss from Bayesian Posterior Samplin...

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

作者： Zhao, Xingyuan Liu, Fang Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9781450392204

Differential privacy (DP) is a state-of-the-art concept that formalizes privacy guarantees. We derive a new bound for the privacy loss from releasing Bayesian posterior samples in the setting of DP. The new bound is tighter than the existing bounds for common Bayesian models and is also consistent with the likelihood principle. We apply the privacy loss quantified by the new bound to release differentially private synthetic data from Bayesian models in several experiments and show the improved utility of the synthetic data compared to those generated from explicitly designed randomization mechanisms that privatize posterior distributions.

关键词： Bayesian posterior sampling data synthesis inherent privacy guarantees privacy for free privacy loss Gaussian multinomial randomness likelihood principle

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A Tenant-based Two-stage Approach to Auditing the Integrity of Virtual Network Function Chains Hosted on Third-Party Clouds 23

A Tenant-based Two-stage Approach to Auditing the Integrity ...

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

作者： Oqaily, Momen Majumdar, Suryadipta Wang, Lingyu Kabir, Mohammad Ekramul Jarraya, Yosr Asadujjaman, A. S. M. Pourzandi, Makan Debbabi, Mourad Concordia Univ CIISE Montreal PQ Canada Ericsson Secur Res Montreal PQ Canada

ISBN: (纸本)9798400700675

There is a growing trend of hosting chains of Virtual Network Functions (VNFs) on third-party clouds for more cost-effective deployment. However, the multi-actor nature of such a deployment may allow a mismatch to silently arise between tenant-level specifications of VNF chains and their cloud provider-level deployment. Most existing auditing approaches would face difficulties in identifying such an integrity breach. First, relying on the cloud provider may not be sufficient, since modifications made by a stealthy attacker may seem legitimate to the provider. Second, the tenant cannot directly perform the auditing due to limited access to the provider-level data, and shipping all such data to the tenant would incur prohibitive confidentiality concerns. In this paper, we propose a tenant-based, two-stage solution where the first stage leverages tenant-level side-channel information to identify suspected integrity breaches, and the second stage then automatically identifies and anonymizes selected provider-level data for the tenant to verify the suspected breaches from the first stage. The key advantages of our solution are: (i) the first stage gives tenants more control and transparency (with the capability of identifying integrity breaches without the provider's assistance), and (ii) the second stage provides tenants higher accuracy (with the capability of rigorous verification based on provider-level data). Our solution is integrated into OpenStack/Tacker (a popular choice for NFV deployment), and its effectiveness is demonstrated via experiments (e.g., up to 90% accuracy with the first stage alone).

关键词： Cloud security NFV security BlackBox auditing security auditing

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Multi-Modality Mobile datasets for Behavioral Biometrics Research data/Toolset paper 23

Multi-Modality Mobile Datasets for Behavioral Biometrics Res...

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

作者： Ray-Dowling, Aratrika Wahab, Ahmed Anu Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn Potsdam NY 13676 USA

ISBN: (纸本)9798400700675

The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities.

关键词： Mobile datasets Continuous Authentication Multi-Modalities Fusion Behavioral Biometrics

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A privacy-Preserving Smart Contract Vulnerability Detection Framework for Permissioned Blockchain 30

A Privacy-Preserving Smart Contract Vulnerability Detection ...

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30th acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Tian, Wensheng Zhang, Lei Chen, Shuangxi Wang, Hu Luo, Xiao Nanhu Lab Jiaxing Peoples R China Jiaxing Vocat & Tech Coll Jiaxing Peoples R China Zhejiang Big Data Dev Adm Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798400700507

The two main types of blockchains that are currently widely deployed are public blockchains and permissioned blockchains. The research that has been conducted for blockchain vulnerability detection is mainly oriented to public blockchains. Less consideration is given to the unique requirements of the permissioned blockchains, which cannot be directly migrated to the application scenarios of the permissioned blockchains. The permissioned blockchain is deployed between verified organizations, and its smart contracts may contain sensitive information such as the transaction flow of the contracts, transaction algorithms. The sensitive information can be considered as the private information of the smart contracts themselves, which should be kept confidential to users outside the blockchain. In this paper, a privacy-preserving smart contract vulnerability detection framework is proposed. The framework leverages blockchain and confidential computing technologies to enable vulnerability detection in permissioned blockchain smart contracts while protecting the privacy of smart contracts. The framework is also able to protect the interests of vulnerability detection model owners. We experimentally validate the detection performance of our framework in a confidential computing environment.

关键词： Smart Contract privacy-preserving Vulnerability Detection Blockchain

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