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

共 560 条记录，以下是111-120 订阅

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A Federated Learning-enabled Smart Street Light Monitoring application: Benefits and Future Challenges 1

A Federated Learning-enabled Smart Street Light Monitoring A...

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1st acm Workshop on AI Empowered Mobile and Wireless Sensing (MORSE) Part of MOBICOM conference

作者： Anand, Diya Mavromatis, Ioannis Carnelli, Pietro Khan, Aftab Univ Bristol Bristol Avon England Toshiba Europe Ltd Bristol Res & Innovat Lab Bristol Avon England

ISBN: (纸本)9781450395229

data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalised machine learning techniques for the classification task of the lampposts operation. Incorporating FL in such a scenario shows minimal performance reduction in terms of the classification task, but huge improvements in the communication cost and the privacy preserving. These outcomes strengthen FL's viability and potential for IoT applications.

关键词： Smart Cities IoT Infrastructure Monitoring Lamppost Neural Networks Federated Learning

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Sybil-Based Attacks on Google Maps or How to Forge the Image of City Life 22

Sybil-Based Attacks on Google Maps or How to Forge the Image...

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

作者： Eryonucu, Cihan Papadimitratos, Panos KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781450392167

Location-based services (LBS) increasingly rely on participatory or crowd-sensed data: users voluntarily contribute data about their whereabouts and points of interest (POIs) and allow the LBS to capture the dynamically changing environment, e.g., how crowded specific places, streets, or public transportation are. Popular LBS applications do not offer strong security, less so for their participatory sensing (PS) and data contribution part. Openness favors participation and increases data, but it also makes attacks easier. Sporadic misbehavior incidents and the presumed user honesty should not be reassuring: an attacker could exploit the PS components and submit a large volume of forged data to dominate the PS-collected LBS data, locally or at a large scale. Individuals, organizations, or entire areas could be targeted, e.g., having customers diverted or causing public transportation routes or roads to appear congested. The lingering open question is whether such attacks can be perpetrated against well-established popular LBS with PS components. This paper affirms this: we investigate Google Maps, the single most popular application in this domain, and show a range of effective and scalable attacks based on very modest adversarial assumptions. We reverse-engineer the data submission process and automate attacks that craft and submit false data in volume and a targeted fashion. We collect evidence that our attacks work on POI crowdedness, traffic congestion levels, and public transportation crowdedness with extreme caution. We responsibly disclosed the attacks to Google, acknowledged them and awarded recognition. The attack methodology carries over to other LBS applications but, most importantly, raises awareness and motivates countermeasures, which we also outline here, for stronger LBS and PS security overall.

关键词： Participatory Sensing Mobile Crowd Sensing Sybil Attacks Google Maps Attacks security Vulnerabilities

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Efficient Zero-Knowledge Proofs on Signed data with applications to Verifiable Computation on data Streams 22

Efficient Zero-Knowledge Proofs on Signed Data with Applicat...

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28th acm SIGSAC conference on Computer and Communications security, CCS 2022

作者： Fiore, Dario Tucker, Ida Imdea Software Institute Madrid Spain

ISBN: (纸本)9781450394505

We study the problem of privacy-preserving proofs on streamed authenticated data. In this setting, a server receives a continuous stream of data from a trusted data provider, and is requested to prove computations over the data to third parties in a correct and private way. In particular, the third party learns no information on the data beyond the validity of claimed results. A challenging requirement here, is that the third party verifies the validity with respect to the specific data authenticated by the provider, while communicating only with the server. This problem is motivated by various application areas, ranging from stock-market monitoring and prediction services;to the publication of government-ran statistics on large healthcare databases. All of these applications require a reliable and scalable solution, in order to see practical adoption. In this paper, we identify and formalize a key primitive allowing one to achieve the above: homomorphic signatures which evaluate non-deterministic computations (HSNP). We provide a generic construction for an HSNP evaluating universal relations;instantiate the construction;and implement a library for HSNP. This in turn allows us to build SPHINX: a system for proving arbitrary computations over streamed authenticated data in a privacy-preserving manner. SPHINX improves significantly over alternative solutions for this model. For instance, compared to corresponding solutions based on Marlin (Eurocrypt'20), the proof generation of SPHINX is between 15× and 1300× faster for various computations used in sliding-window statistics. © 2022 acm.

关键词： privacy-preserving techniques

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GZKP: A GPU Accelerated Zero-Knowledge Proof System 2023

GZKP: A GPU Accelerated Zero-Knowledge Proof System

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28th acm International conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Ma, Weiliang Xiong, Qian Shi, Xuanhua Ma, Xiaosong Jin, Hai Kuang, Haozhao Gao, Mingyu Zhang, Ye Shen, Haichen Hu, Weifang Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Comp Sci & TechnolCluster & Grid Comp Lab Wuhan Hubei Peoples R China Hamad Bin Khalifa Univ Doha Qatar Tsinghua Univ Beijing Peoples R China Scroll Fdn Victoria Beau Vallon Seychelles

ISBN: (纸本)9781450399166

Zero-knowledge proof (ZKP) is a cryptographic protocol that allows one party to prove the correctness of a statement to another party without revealing any information beyond the correctness of the statement itself. It guarantees computation integrity and confidentiality, and is therefore increasingly adopted in industry for a variety of privacy-preserving applications, such as verifiable outsource computing and digital currency. A significant obstacle in using ZKP for online applications is the performance overhead of its proof generation. We develop GZKP, a GPU accelerated zero-knowledge proof system that supports different levels of security requirements and brings significant speedup toward making ZKP truly usable. For polynomial computation over a large finite field, GZKP promotes a cache-friendly memory access pattern while eliminating the costly external shuffle in existing solutions. For multi-scalar multiplication, GZKP adopts a new parallelization strategy, which aggressively combines integer elliptic curve point operations and exploits fine-grained task parallelism with load balancing for sparse integer distribution. GZKP outperforms the state-of-the-art ZKP systems by an order of magnitude, achieving up to 48.1x and 17.6x speedup with standard cryptographic benchmarks and a real-world application workload, respectively.

关键词： zero-knowledge proof GPU acceleration

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Multi-Device Context-Sensitive Attacks Against privacy 25

Multi-Device Context-Sensitive Attacks Against Privacy

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

作者： Edgardo A. Barsallo Yi Joshua D. O. Majors Aditya Vardhan Padala Darren Wu Aravind Machiry Saurabh Bagchi Delivery Dynamics Panama Panama Panama Purdue University West Lafayette IN USA

ISBN: (纸本)9798400714764

As the adoption of wearable and smart devices increases, their privacy and security are still a concern. These devices collect sensitive data and constantly communicate with each other, posing new privacy threats that need to be understood and addressed. In this paper, we analyze the privacy of smart devices from a multi-device perspective. The central premise of our work is that information available at each device may be non-sensitive or lightly so, but by orchestrating information from multiple connected smart devices, it is possible to infer sensitive content. To verify this, we conduct a user study to understand user perceptions towards privacy on smart devices and contrast them with their actual behavior while operating these devices. We then present an attack framework that can leverage tightly coupled and connected smart devices, such as mobile, wearable, and smart TV, to leak sensitive information inferred from individually non-sensitive data. Finally, we introduce a tool based on NLP techniques to identify potential privacy vulnerabilities on smart devices and propose an integrated solution to increase smart devices' security. This analysis helps close the gap between user's perception and reality regarding privacy risks within their smart ecosystem.

关键词： privacy

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Is there a security Mindset and Can it be Taught? 21

Is there a Security Mindset and Can it be Taught?

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

作者： Siraj, Ambareen Sridhar, Nigamanth Hamilton, John A. Drew, Jr. Khan, Latifur Kaza, Siddharth Gupta, Maanak Mittal, Sudip Tennessee Technol Univ Cookeville TN 38505 USA Cleveland State Univ Cleveland OH 44115 USA Mississippi State Univ Mississippi State MS 39762 USA Univ Texas Dallas Richardson TX 75083 USA Towson Univ Towson MD USA Univ North Carolina Wilmington Wilmington NC USA

The field of cybersecurity is becoming very dynamic, and needs continuous evolution. This requires not only the formal and informal education, but a security mindset to be developed for our future workforce. This pane... 详细信息

ISBN: (纸本)9781450381437

关键词： Cybersecurity

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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...

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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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Don't fool yourself with Forward privacy, Your queries STILL belong to us! 21

Don't fool yourself with Forward Privacy, Your queries STILL...

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

作者： Salmani, Khosro Barker, Ken Mt Royal Univ Calgary AB Canada Univ Calgary Calgary AB Canada

ISBN: (纸本)9781450381437

Dynamic Searchable Symmetric Encryption (DSSE) enables a user to perform encrypted search queries on encrypted data stored on a server. Recently, a notion of Forward privacy (FP) was introduced to guarantee that a newly added document cannot be linked to previous queries, and to thwart relative attacks and lessen information leakage and its consequences. However, in this paper we show that the forward-private schemes have no advantage (in preventing the related attacks) compared to traditional approaches, and previous attacks are still applicable on FP schemes. In FP approaches, access pattern leakage is still possible and can be employed to uncover the search pattern which can be used by passive and adaptive attacks. To address this issue, we construct a new parallelizable DSSE approach to obfuscate the access and search pattern. Our cost-efficient scheme supports both updates and searches. Our security proof and performance analysis demonstrate the practicality, efficiency, and security of our approach.

关键词： data privacy Cloud security Dynamic SSE Access and search pattern leakage

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Differentially Private Iterative Screening Rules for Linear Regression 25

Differentially Private Iterative Screening Rules for Linear ...

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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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