PAPIR: privacy-aware personalized information retrieval

El-Ansari, Anas; Beni‐Hssane, Abderrahim; Saadi, Mostafa; Fissaoui, Mohamed El

doi:10.1007/s12652-020-02736-y

Cited by 18 publications

(11 citation statements)

References 42 publications

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“…Efforts of personalizing computer systems are, however, often met with critique as to their impact on information diversity (e.g., a recent reflection in Zanker et al, 2019 or study in Lai and Luczak-Roesch, 2019 ). Learning supporting search systems may collect and evaluate potentially sensitive data and should, therefore, be primed to use privacy-preserving mechanisms ( El-Ansari et al, 2021 ).…”

Section: Componentsmentioning

confidence: 99%

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

et al. 2022

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Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.

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Section: Componentsmentioning

confidence: 99%

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

et al. 2022

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“…By using this method, health-relevant data is protected from privacy violations and can only be used in a privacy preserving manner. The other notable solutions that have demonstrated effectiveness in terms of privacy are, general data protection regulation (GDPR)-based computation [83], privacyaware personalized information retrieval (PAPIR) method [84], privacy preserving data visualizations (PPDV) method [85], spatial k-anonymity method [86], and privacy preserving location-based services (LBS) method [87]. All these solutions have demonstrated effectiveness for preserving people's privacy when analytics/mining is performed on the collected data.…”

Section: Likely Privacy Concerns/issues In the Control Measurementioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

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With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.

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“…This heuristic is efficient when it comes to preventing an attacker from tracing the issuer of a search query. Additional privacypreserving IR solutions include homomorphic encryption (HE) [41], DP [42], and hashing [39].…”

Section: Related Workmentioning

confidence: 99%

Privacy in Open Search: A Review of Challenges and Solutions

Sousa,

Guetl,

Kern

2021

Preprint

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Privacy is of worldwide concern regarding activities and processes that include sensitive data. For this reason, many countries and territories have been recently approving regulations controlling the extent to which organizations may exploit data provided by people. Artificial intelligence areas, such as machine learning and natural language processing, have already successfully employed privacy-preserving mechanisms in order to safeguard data privacy in a vast number of applications. Information retrieval (IR) is likewise prone to privacy threats, such as attacks and unintended disclosures of documents and search history, which may cripple the security of users and be penalized by data protection laws. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data. Our contribution is threefold: firstly, we present an overview of privacy threats to IR tasks; secondly, we discuss applicable privacy-preserving mechanisms which may be employed in solutions to restrain privacy hazards; finally, we bring insights on the tradeoffs between privacy preservation and utility performance for IR tasks.

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PAPIR: privacy-aware personalized information retrieval

Cited by 18 publications

References 42 publications

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Privacy in Open Search: A Review of Challenges and Solutions

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