Enhancing Selectivity in Big Data

Lécuyer, Mathias; Spahn, Riley; Geambasu, Roxana; Huang, Tsung-Hui; Sen, Siddhartha

doi:10.1109/msp.2018.1331036

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…1) System Performance: Evaluating an ad-transparency tool is extremely challenging since the ground truth of targeting decisions is unknown. The effectiveness of these tools has been occasionally assessed through manual inspection [84], [85]. However, this approach has been recently shown to be extremely prone to errors [86].…”

Section: B Resultsmentioning

confidence: 99%

“…Another more recent work for conducting experiments based on artificial profiles is [84], which tracks the personal data collected by several Web services, and tries to correlate data inputs (e.g., e-mails and search queries) with data outputs (e.g., ads and recommended links). The proposed platform tackles this correlation problem in a broad sense, and is tested for the ads displayed on Gmail.…”

Section: B Advertising Transparencymentioning

confidence: 99%

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MyAdChoices

2017

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The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and antitracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web—in which users get content free in return for allowing advertisers to show them ads—is at serious peril. In this article, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.

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Section: B Resultsmentioning

confidence: 99%

Section: B Advertising Transparencymentioning

confidence: 99%

MyAdChoices

2017

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“…Actually, the opposite is true. The importance of data in the modern information culture has motivated big firms to collect as much of it as they can [4,5]. Multinational companies are gathering an increasing amount of personal data on their customers, including their online behavior, contacts, preferences, and movements, through sensors embedded in their devices (references 6 and 7).…”

Section: Introductionmentioning

confidence: 99%

Securing Data Over Network with Blockchain and AI

2023

ecb

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The research proposes a framework called SecNet for ensuring the safety of data over its lifecycle via the Internet at scale. The widespread dispersion of data across digital spaces has rendered authentication and authorization of its usage difficult. The proposed architecture is comprised of three primary elements: In order to generate authentic big data, blockchainbased data exchange with ownership guarantee is necessary. Blockchain technology makes it possible to verify the authenticity of the data and trace it back to its original creator. Data encryption enabled by artificial intelligence: This system has the potential to create a more secure and trustworthy digital ecosystem by developing more nuanced security rules. When used to cyber security, AI has the potential to discover new vulnerabilities and flaws in the system. The effectiveness of AI may be improved if individuals were incentivized to share their data via the establishment of a trustworthy system for exchanging monies in exchange for the supply of a security service. This article compares and contrasts the typical SecNet implementation with some of its alternatives. We also analyze the repercussions on network security and revenue generation. Overcoming the challenges of allowing data interchange through the internet is central to the proposed architecture, which aims to boost AI performance utilizing real-world huge data.

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“…In such an informationoriented society, data is the property of the owner and its use must be under the complete control of the owner, which is not a common case [2], [3]. Given that data is arguably the crude oil of the information society, almost every large company wants to collect as much data as possible for future competitiveness [4], [5]. Sensors built into the products of these large companies increase and implicitly record the amount of personal data such as location, web search behavior, user calls, and user preferences.…”

Section: Introductionmentioning

confidence: 99%

Securing Data using Artificial Intelligence andBlock Chain

Chaitanya¹,

Mubaraq²

2022

IJETMS

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The input for several Artificial Intelligence algorithms is Data which is used formining the valuable features. But, data on the internet are unbelievable and difficult to authorize. It is very difficult to verify the data for the users in this complex cyberspace. So, for this we proposed SecNet in this paper. SecNet , Anarchitecture that helps in securing data storage, processing of data and sharing large scale Internet environments. The main aim of this architecture is to createa more secure cyberspace with real big data and to improve the Artificial Intelligence algorithms on various data sources. This architecture integrates andprovides three main components1) The exchange of Blockchain-based data is done with guaranteed Ownership. This allows exchange of reliable data in largescale space and forms real big data.2) The securing of AI-based secure computing platform powered by Artificial Intelligence to create smarter security rules that helps to create a more stable cyberspace. 3)The trusted value-sharing Security Service purchase Mechanism, provides participants with a great opportunity to receive Economic Rewards for data or service provision,thus facilitating data sharing, resulting in better AI performance. This also discusses common SecNet use cases and potential another deploymentmethods, and analyzes their effectiveness in terms of security in networks and their economic return.

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Enhancing Selectivity in Big Data

Cited by 9 publications

References 8 publications

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Securing Data Over Network with Blockchain and AI

Securing Data using Artificial Intelligence andBlock Chain

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