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

共 559 条记录，以下是71-80 订阅

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RS-PKE: Ranked Searchable Public-Key Encryption for Cloud-Assisted Lightweight Platforms 22

RS-PKE: Ranked Searchable Public-Key Encryption for Cloud-As...

引用

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

作者： Mouri, Israt Jahan Ridowan, Muhammad Adnan, Muhammad Abdullah Bangladesh Univ Engn & Technol BUET Dhaka Bangladesh

ISBN: (纸本)9781450392204

Since more and more data from lightweight platforms like IoT devices or mobile apps are being outsourced to the cloud, the need to ensure privacy while retaining data usability is essential. In this paper, we design a framework where lightweight platforms like IoT devices can encrypt documents and generate document indexes using the public key before uploading the document to the cloud, and an admin can search and retrieve the top-k most relevant documents that match a specific keyword using the private key. In most existing searchable encryption that supports IoT, all the documents that match a queried keyword are returned to the admin. This is not practical as IoT devices continuously upload data. We formally name our framework as Ranked Searchable Public-Key Encryption (RS-PKE). We also implemented a prototype of RS-PKE and tested it in the Amazon EC2 cloud using the RFC dataset. The comprehensive evaluation demonstrates that RS-PKE is efficient and secure for practical deployment.

关键词： Forward privacy Dynamic PKE Keyword privacy Cloud security Ranked Searchable Encryption Lightweight Platforms IoT

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Enforcement of Laws and privacy Preferences in Modern Computing Systems 22

Enforcement of Laws and Privacy Preferences in Modern Comput...

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

作者： Kantarcioglu, Murat Carminati, Barbara Samtani, Sagar Mittal, Sudip Gupta, Maanak Univ Texas Dallas Richardson TX 75080 USA Univ Insubria Insubria Italy Indiana Univ Bloomington IN USA Mississippi State Univ Mississippi State MS 39762 USA Tennessee Technol Univ Cookeville TN USA

ISBN: (纸本)9781450392204

Modern civilization is highly dependent on computing systems, touching all aspects of business, government, and individual life. At the same time, there has been an increase in laws and privacy preferences whose implementation and effectiveness depend on software. Whereas organizations and individuals have been expected to comply with laws and regulations, now computing systems must also be compliant and accountable. Computing systems need to be designed with privacy preferences and legal statutes in mind, and should be adaptable to change.

关键词： Laws and legislation

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Exploring Unlearning Methods to Ensure the privacy, security, and Usability of Recommender Systems 23

Exploring Unlearning Methods to Ensure the Privacy, Security...

引用

17th acm conference on Recommender Systems (RecSys)

作者： Leysen, Jens Univ Antwerp Antwerp Belgium

ISBN: (纸本)9798400702419

Machine learning algorithms have proven highly effective in analyzing large amounts of data and identifying complex patterns and relationships. One application of machine learning that has received significant attention in recent years is recommender systems, which are algorithms that analyze user behavior and other data to suggest items or content that a user may be interested in. However useful, these systems may unintentionally retain sensitive, outdated, or faulty information. Posing a risk to user privacy, system security, and limiting a system's usability. In this research proposal, we aim to address these challenges by investigating methods for machine "unlearning", which would allow information to be efficiently "forgotten" or "unlearned" from machine learning models. The main objective of this proposal is to develop the foundation for future machine unlearning methods. We first evaluate current unlearning methods and explore novel adversarial attacks on these methods' verifiability, efficiency, and accuracy to gain new insights and further develop the theory of machine unlearning. Using our gathered insights, we seek to create novel unlearning methods that are verifiable, efficient, and limit unnecessary accuracy degradation. Through this research, we seek to make significant contributions to the theoretical foundations of machine unlearning while also developing unlearning methods that can be applied to real-world problems.

关键词： Recommender Systems Machine Unlearning Research Proposal

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security and privacy for Emerging IoT and CPS Domains 22

Security and Privacy for Emerging IoT and CPS Domains

引用

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

作者： Bertino, Elisa Sandhu, Ravi Bhavani Ray, Indrakshi Li, Wenjia Gupta, Maanak Mittal, Sudip Purdue Univ W Lafayette IN 47907 USA Univ Texas San Antonio San Antonio TX USA Univ Texas Dallas Richardson TX 75083 USA Colorado State Univ Ft Collins CO 80523 USA New York Inst Technol New York NY USA Tennessee Technol Univ Cookeville TN USA Mississippi State Univ Mississippi State MS 39762 USA

ISBN: (纸本)9781450392204

The proliferation of IoT and CPS technologies demand novel conceptual, foundational and applied cybersecurity solutions. The dynamic behaviour of these distributed systems augmented with physical and computational constraints of smart devices, require cybersecurity approaches for timely prevention and detection of attacks. This panel aims to discuss open challenges and highlight future research directions for cybersecurity in IoT and CPS.

关键词： Cybersecurity

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Hardening with Scapolite: A DevOps-based Approach for Improved Authoring and Testing of security-Configuration Guides in Large-Scale Organizations 22

Hardening with Scapolite: A DevOps-based Approach for Improv...

引用

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

作者： Stoeckle, Patrick Pruteanu, Ionut Grobauer, Bernd Pretschner, Alexander Tech Univ Munich TUM Munich Germany Siemens AG Bucharest Romania Siemens AG Munich Germany

ISBN: (纸本)9781450392204

TOOL PAPER(1) security Hardening is the process of configuring IT systems to ensure the security of the systems' components and data they process or store. In many cases, so-called security-configuration guides are used as a basis for security hardening. These guides describe secure configuration settings for components such as operating systems and standard applications. Rigorous testing of security-configuration guides and automated mechanisms for their implementation and validation are necessary since erroneous implementations or checks of hardening guides may severely impact systems' security and functionality. At Siemens, centrally maintained security-configuration guides carry machine-readable information specifying both the implementation and validation of each required configuration step. The guides are maintained within git repositories;automated pipelines generate the artifacts for implementation and checking, e.g., PowerShell scripts for Windows, and carry out testing of these artifacts on AWS images. This paper describes our experiences with our DevOps-inspired approach for authoring, maintaining, and testing security-configuration guides. We want to share these experiences to help other organizations with their security hardening and increase their systems' security.

关键词： Hardening security Configuration

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Towards Vertical privacy-Preserving Symbolic Regression via Secure Multiparty Computation

Towards Vertical Privacy-Preserving Symbolic Regression via ...

引用

Genetic and Evolutionary Computation conference (GECCO)

作者： Du Nguyen Duy Affenzeller, Michael Nikzad-Langerodi, Ramin Software Competence Ctr Hagenberg Hagenberg Austria Univ Appl Sci Upper Austria Hagenberg Austria Software Competence Ctr Hagenberg Hagenberg Austria

ISBN: (纸本)9798400701207

Symbolic Regression is a powerful data-driven technique that searches for mathematical expressions that explain the relationship between input variables and a target of interest. Due to its efficiency and flexibility, Genetic Programming can be seen as the standard search technique for Symbolic Regression. However, the conventional Genetic Programming algorithm requires storing all data in a central location, which is not always feasible due to growing concerns about data privacy and security. While privacy-preserving research has advanced recently and might offer a solution to this problem, their application to Symbolic Regression remains largely unexplored. Furthermore, the existing work only focuses on the horizontally partitioned setting, whereas the vertically partitioned setting, another popular scenario, has yet to be investigated. Herein, we propose an approach that employs a privacy-preserving technique called Secure Multiparty Computation to enable parties to jointly build Symbolic Regression models in the vertical scenario without revealing private data. Preliminary experimental results indicate that our proposed method delivers comparable performance to the centralized solution while safeguarding data privacy.

关键词： Symbolic Regression Genetic Programming privacy-Preserving Federated Learning Secure Multiparty Computation

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Disclosure Risk from Homogeneity Attack in Differentially Private Release of Frequency Distribution 22

Disclosure Risk from Homogeneity Attack in Differentially Pr...

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

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

ISBN: (纸本)9781450392204

Differential privacy (DP) provides a robust model to achieve privacy guarantees in released information. We examine the robustness of the protection against homogeneity attack (HA) in multidimensional frequency distributions sanitized via DP randomization mechanisms. We propose measures for disclosure risk from HA and derive closed-form relationships between privacy loss parameters in DP and disclosure risk from HA. We also provide a lower bound to the disclosure risk on a sensitive attribute when all the cells formed by quasi-identifiers are homogeneous for the sensitive attribute. The availability of the closed-form relationships helps understand the abstract concepts of DP and privacy loss parameters by putting them in the context of a concrete privacy attack and offers a perspective for choosing privacy loss parameters when employing DP mechanisms to release information in practice. We apply the closed-form mathematical relationships on real-life datasets to assess disclosure risk due to HA in differentially private sanitized frequency distributions at various privacy loss parameters.

关键词： differential privacy disclosure risk homogeneity attack Gaussian mechanism Laplace mechanism privacy loss privacy budget

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Advancing Healthcare in Cordoba :Telemedicine as a Service (PGaaS) and Addressing OWASP Machine Learning security Challenges 50

Advancing Healthcare in Cordoba :Telemedicine as a Service (...

引用

50th Latin American Computing conference (CLEI)

作者： Julio, Yair Rivera Mangones, Angel Pinto Tovio, Juan Torres Hernandez, Frank Ibarra Aguilar, Jose Gomez, Sirley Kerguelen Inst Univ Amer Dept Ingn Sistemas Barranquilla Colombia Univ Sinu Dept Ingn Sistemas Monteria Colombia Univ Sinu Dept Ingn Elect Monteria Colombia IMDEA Networks Inst Madrid Spain Univ Los Andes CEMISID Merida Venezuela Corp Unificada Nacl Dept Ingn Sistemas Monteria Colombia

ISBN: (纸本)9798331540982;9798331540975

This study proposes a framework that leverages ML algorithms for the cybersecurity of telemedicine systems in the Department of Cordoba, Colombia, addressing challenges amplified by the COVID-19 pandemic. The framework emphasizes enhancing the processing capabilities and reliability in managing clinical information, particularly focusing on the RES. It integrates a PGaaS aligned with the OWASP guidelines, aiming to neutralize emerging threats using advanced IA models. The system incorporates data encryption, 2FA-based Access Control, continuous backups, code obfuscation, and watermarking, all supported by a suitable legal framework. These components are combined into a comprehensive monitoring and auditing scheme to ensure robust protection against intelligent intrusions and the compromise of medical data.

关键词： Machine Learning (ML) COVID-19 Clinical Information Processing Special Health Services Registry (RES) privacy-as-a-Service Platform (PGaaS) Open Web application security Project (OWASP) Artificial Intelligence (IA) data Encryption Two-Factor Authentication (2FA)

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Genomic data Sharing under Dependent Local Differential privacy 22

Genomic Data Sharing under Dependent Local Differential Priv...

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

作者： Yilmaz, Emre Ji, Tianxi Ayday, Erman Li, Pan Univ Houston Downtown Houston TX 77002 USA Case Western Reserve Univ Cleveland OH 44106 USA

ISBN: (纸本)9781450392204

privacy-preserving genomic data sharing is prominent to increase the pace of genomic research, and hence to pave the way towards personalized genomic medicine. In this paper, we introduce (epsilon, T)-dependent local differential privacy (LDP) for privacy-preserving sharing of correlated data and propose a genomic data sharing mechanism under this privacy definition. We first show that the original definition of LDP is not suitable for genomic data sharing, and then we propose a new mechanism to share genomic data. The proposed mechanism considers the correlations in data during data sharing, eliminates statistically unlikely data values beforehand, and adjusts the probability distributions for each shared data point accordingly. By doing so, we show that we can avoid an attacker from inferring the correct values of the shared data points by utilizing the correlations in the data. By adjusting the probability distributions of the shared states of each data point, we also improve the utility of shared data for the data collector. Furthermore, we develop a greedy algorithm that strategically identifies the processing order of the shared data points with the aim of maximizing the utility of the shared data. Our evaluation results on a real-life genomic dataset show the superiority of the proposed mechanism compared to the randomized response mechanism (a widely used technique to achieve LDP).

关键词： Genomics data sharing Local differential privacy

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TreeChain: A High Throughput and Efficient Search based Secure application for Internet of Vehicles 23

TreeChain: A High Throughput and Efficient Search based Secu...

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24th International conference on Distributed Computing and Networking (ICDCN)

作者： Kumar, Amritesh Das, Debasis IIT Jodhpur Rajasthan India

ISBN: (纸本)9781450397964

The Internet of Vehicles (IoV) rapidly expands due to lower bandwidth, enhanced security, and privacy for high-quality information sharing with minimal latency. To properly execute all these services, IoV must first overcome several challenges created by the centralized architecture, notably those related to throughput and data storage. We offer TreeChain, a high throughput and efficient information-searching technique based on a novel balance tree structure that also addresses the most extended chain sequence problem. Following that, TreeChain compares security strength in terms of several types of security attacks to other state-of-the-art methodologies, as well as performance results in terms of throughput and data storage. According to the results, TreeChain outperforms state-of-the-art solutions in terms of security and performance.

关键词： Internet of Vehicles (IoV) Balance Tree Blockchain Throughput

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