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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131-140 订阅

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Programming Support for Local-First Software Enabling the Design of privacy-Preserving Distributed Software without Relying on the Cloud 26

Programming Support for Local-First Software Enabling the De...

引用

acm SIGPLAN International conference on Systems, Programming, Languages, and applications: Software for Humanity (SPLASH Companion)

作者： Haas, Julian Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9781450399012

Local-first software manages and processes private data locally while still enabling collaboration between multiple parties connected via partially unreliable networks. Such software typically involves interactions with users and the execution environment (the outside world). The decentralized nature of local-first software paired with the unpredictability of interactions driven from the outside world impede reasoning about their correctness. Yet, existing solutions to develop local-first software do not provide safety guarantees and instead expect developers to reason about concurrent interactions in an environment with unreliable network conditions. This is too much to ask of application developers, who are usually not experts in designing distributed systems. This work seeks to develop a programming model which facilitates the construction of local-first software and eradicates certain classes of safety and security problems by design. We do so by providing a dedicated local-first programming language and an accompanying automated verification procedure that can be integrated as part of the compilation process.

关键词： local-first reactive programming

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Differentially Private Iterative Screening Rules for Linear Regression 25

Differentially Private Iterative Screening Rules for Linear ...

引用

proceedings of the Fifteenth acm conference on data and application security and privacy

作者： Amol Khanna Fred Lu Edward Raff Booz Allen Hamilton Boston MA USA Booz Allen Hamilton Los Angeles CA USA and University of Maryland Baltimore County Baltimore MD USA Booz Allen Hamilton Syracuse NY USA and University of Maryland Baltimore County Baltimore MD USA

ISBN: (纸本)9798400714764

Linear L1-regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way to eliminate features when producing the sparse regression weights of L1 models. However, despite the increasing need of privacy-preserving models for data analysis, to the best of our knowledge, no differentially private screening rule exists. In this paper, we develop the first private screening rule for linear regression. We initially find that this screening rule is too strong: it screens too many coefficients as a result of the private screening step. However, a weakened implementation of private screening reduces overscreening and improves performance.

关键词： differential privacy

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Two-Client Inner-Product Functional Encryption with an application to Money-Laundering Detection 22

Two-Client Inner-Product Functional Encryption with an Appli...

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28th acm SIGSAC conference on Computer and Communications security (CCS)

作者： de Perthuis, Paola Pointcheval, David Cosmian Paris France CNRS INRIA ENS PSL CosmianDIENS Paris France

ISBN: (纸本)9781450394505

In this paper, we extend Inner-Product Functional Encryption (IPFE), where there is just a vector in the key and a vector in the single sender's ciphertext, to two-client ciphertexts. More precisely, in our two-client functional encryption scheme, there are two data Providers who can independently encrypt vectors alpha and y for a data consumer who can, from a functional decryption key associated to a vector alpha, compute Sigma alpha(i)x(i)y(i) = x. center dot Diag(alpha) center dot y(inverted perpendicular). Ciphertexts are linear in the dimension of the vectors, whereas the functional decryption keys are of constant size. We study two interesting particular cases: 2-party Inner-Product Functional Encryption, with alpha = (1,..., 1). There is a unique functional decryption key, which enables the computation of x center dot y(inverted perpendicular) by a third party, where.. and.. are provided by two independent clients;Inner-Product Functional Encryption with a Selector, with x = x(0)parallel to x(1) and y = (b) over bar (n)parallel to b(n) is an element of {1(n)parallel to 0(n),0(n)parallel to 1(n)}}, for some bit b, on the public coefficients alpha = alpha(0)parallel to alpha(1), in the functional decryption key, so that one gets x(b) center dot alpha(inverted perpendicular)(b), where x and b are provided by two independent clients. This result is based on the fundamental Product-Preserving Lemma, which is of independent interest. It exploits Dual Pairing Vector Spaces (DPVS), with security proofs under the SXDH assumption. We provide two practical applications to medical diagnosis for the latter IPFE with a selector, and to money-laundering detection for the former 2-party IPFE, both with strong privacy properties, adaptative security and the use of labels granting a Multi-Client Functional Encryption (MCFE) security for the scheme, thus enabling its use in practical situations.

关键词： functional encryption inner-product private evaluation

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SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform 21

SteemOps: Extracting and Analyzing Key Operations in Steemit...

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11th acm conference on data and application security and privacy (CODASPY)

作者： Li, Chao Palanisamy, Balaji Xu, Runhua Xu, Jinlai Wang, Jingzhe Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China Univ Pittsburgh Sch Comp & Informat Pittsburgh PA USA IBM Almaden Res Ctr San Jose CA USA

ISBN: (纸本)9781450381437

Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.

关键词： blockchain social network Steem dataset

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Evaluating Website data Leaks through Spam Collection on Honeypots 25

Evaluating Website Data Leaks through Spam Collection on Hon...

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proceedings of the Fifteenth acm conference on data and application security and privacy

作者： Oghenerukevwe Elohor Oyinloye Carol Fung Concordia Institute for Information Systems and Engineering Concordia University Montreal Quebec Canada

ISBN: (纸本)9798400714764

Nowadays, people rely heavily on online services in their daily lives such as communication, education, shopping, and entertainment. While online services offer convenience in daily living, users often receive a large number of spams as a result. While previous studies have linked spam receipt primarily to user behavior, this research proposes that spam can serve as a forensic indicator of data leaks by websites. To test our hypothesis, we conducted an experiment to deploy 148 honeypots across 370 websites spanning 12 communities. We monitored and audited the spams received by our honeypots for 47 weeks and analyzed their nature, pattern and origin. The results reveal that some legitimate websites leak user data despite having privacy policy statements. The findings also highlight that some websites automatically enroll users in newsletters or mailing lists without asking consent during the sign-up. This issue arises from conflating privacy policies with spam subscription and third party share agreements. To address these issues we suggest that regulators enforce websites to separate subscription agreement from privacy policy statements, and direct consent for third party share be requested at sign up. Also, websites should evaluate third party chain to ensure user data protection.

关键词： data privacy

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2022 4th Blockchain and Internet of Things conference, BIOTC 2022

2022 4th Blockchain and Internet of Things Conference, BIOTC...

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4th Blockchain and Internet of Things conference, BIOTC 2022

ISBN: (纸本)9781450396622

The proceedings contain 18 papers. The topics discussed include: distributed random number generation method on smart contracts;loan chain: a blockchain-based framework for smart credit lending;blockchain-based security governance framework of agricultural product traceability data;research progress and trend prospect of blockchain technology application in logistics and supply chain information system under pandemic-hit;a blockchain-based electricity retail contracts management system;computational experimental evaluation of the time variance of cryptocurrency mining using cryptographic hash functions;optimization of student physical health data cycle under computer blockchain technology;design and implementation of electronic medical record system based on Hyperledger fabric;trusted blockchain-based data fingerprinting differential-traceability and SkipList indexing methods in privacy protection;and a blockchain-enabled location privacy-preserving under local differential privacy for internet of vehicles.

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Harmonizing Differential privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence 25

Harmonizing Differential Privacy Mechanisms for Federated Le...

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proceedings of the Fifteenth acm conference on data and application security and privacy

作者： Shuya Feng Meisam Mohammady Hanbin Hong Shenao Yan Ashish Kundu Binghui Wang Yuan Hong University of Connecticut Storrs CT USA Iowa State University Ames IA USA Cisco Ressearch San Jose CA USA Illinois Institute of Technology Chicago IL USA

ISBN: (纸本)9798400714764

Differentially private federated learning (DP-FL) offers a compelling approach to collaborative model training by ensuring robust privacy for clients. Despite its potential, current methods face challenges in effectively balancing privacy, utility, and performance across diverse federated learning scenarios. Addressing these challenges, we introduce UDP-FL, to our knowledge the first DP-FL framework that universally harmonizes any randomization mechanism, including those considered optimal, by employing the Gaussian Moments Accountant (viz. DP-SGD). Central to UDP-FL is the 'Harmonizer,' a dynamic module engineered to intelligently select and apply the most suitable DP mechanism tailored to each client's specific privacy requirements, data sensitivities, and computational capacities. This selection process is driven by the principle of Rényi Differential privacy, which serves as a crucial mediator for aligning privacy budgets effectively. Our comprehensive evaluation of UDP-FL, benchmarked against established baseline methods, demonstrates superior performance in upholding privacy guarantees and enhancing model functionality. The framework's robustness has been rigorously tested against a broad spectrum of privacy attacks, making it one of the most thorough validations of a DP-FL framework to date.

关键词： differential privacy

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Why You've Got Mail: Evaluating Inbox privacy Implications of Email Marketing Practices in Online Apps and Services 25

Why You've Got Mail: Evaluating Inbox Privacy Implications o...

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proceedings of the Fifteenth acm conference on data and application security and privacy

作者： Scott Seidenberger Oluwasijibomi Ajisegiri Noah Pursell Fazil Raja Anindya Maiti University of Oklahoma Norman OK USA

ISBN: (纸本)9798400714764

This study explores the widespread perception that personal data, such as email addresses, may be shared or sold without informed user consent, investigating whether these concerns are reflected in actual practices of popular online services and apps. Over the course of a year, we collected and analyzed the source, volume, frequency, and content of emails received by users after signing up for the 150 most popular online services and apps across various sectors. By examining patterns in email communications, we aim to identify consistent strategies used across industries, including potential signs of third-party data sharing. This analysis provides a critical evaluation of how email marketing tactics may intersect with data-sharing practices, with important implications for consumer privacy and regulatory oversight. Our study findings, conducted post-CCPA and GDPR, indicate that while no unknown third-party spam email was detected, internal and authorized third-party email marketing practices were pervasive, with companies frequently sending promotional and CRM emails despite opt-out preferences. The framework established in this work is designed to be scalable, allowing for continuous monitoring, and can be extended to include a more diverse set of apps and services for broader analysis, ultimately contributing to transparency in email address privacy practices.

关键词： email marketing

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Reflections on a Decade of Mobile security Research 22

Reflections on a Decade of Mobile Security Research

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15th acm conference on security and privacy in Wireless and Mobile Networks (acm WiSec)

作者： Enck, William North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781450392167

The emergence of the smartphone in the late 2000s occurred during a perfect storm of technology and society. Advances in embedded technologies provided a critical balance of computing power and energy consumption. The integration of accelerometers and GPS sensors provided valuable primitives for innovative applications, and 3G cellular technologies provided enough data capacity for meaningful interactions with servers. Simultaneously, social networking was taking off, giving consumers a reason to increasingly engage with computing. The mobile industry capitalized on this opportunity, opening the traditionally tightly controlled environment to third-parties and providing a streamlined way for application developers and consumers to discover and commodify computing. Today, Web traffic from smartphones exceeds that of traditional desktops and laptops. The initial reaction of the security research community was cautious, not seeing what made this form of computing different. In many ways, smartphones are the same as traditional consumer platforms. Consumers download and run third-party software that connects to servers on the Internet. However, there are key ways in which smartphones are different. Some of these differences are technical. For example, smartphones never turn off and are continually collecting information. They also present a new runtime abstraction where each application is a security principal. Other differences are rooted in how we use them. Our smartphones are always with us, and as a result they have become the transport vehicle for micro-doses of dopamine that feeds our Internet addicted society. They are the first thing we look at in the morning, the last in the evening, and means of avoiding boredom throughout the day. Despite initial reservations, the past decade has seen a boom in security research studying smartphones and mobile technologies. While this research started at the application layer, it has gradually worked its way down the stack, c

关键词： Smartphone security mobile app security mobile device security

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Measurable and Deployable security: Gaps, Successes, and Opportunities 21

Measurable and Deployable Security: Gaps, Successes, and Opp...

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11th acm conference on data and application security and privacy (CODASPY)

作者： Yao, Danfeng (Daphne) Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)9781450381437

security measurement helps identify deployment gaps and present extremely valuable research opportunities. However, such research is often deemed as not novelty by academia. I will first share my research journey designing and producing a high precision tool CryptoGuard for scanning cryptographic vulnerabilities in large Java projects. That work led us to publish two benchmarks used for systematically assessing state-of-the-art academic and commercial solutions, as well as help Oracle Labs integrate our detection in their routine scanning. Other specific measurement and deployment cases to discuss include the Payment Card Industry data security Standard, which was involved in high-profile data breach incidents, and fine-grained Address Space Layout Randomization (ASLR). The talk will also point out the need for measurement in AI development in the context of code repair. Broadening research styles by accepting and encouraging deployment-related work will facilitate our field to progress towards maturity.

关键词： Measurement deployment security practice precision benchmark software security code scanning static program analysis cryptography implementation CryptoGuard payment card industry PCI web security address space layout randomization ASLR code repair AI machine learning

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