Privacy Preservation in the Age of Big Data: A Survey

Davis, John S.; Osoba, Osonde A.

doi:10.7249/wr1161

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…The totals do not necessarily have to add up to the sum of the individual values (non-additivity) and one receives 1 https://mip.swisscom.ch, accessed 11.08.2021 different results for identical queries (non-consistency). Second, differential privacy frequently lead to low utility [20]. For example, Uber has developed an open source framework 2 to facilitate the integration of differential privacy into existing databases, e.g.…”

Section: Differential Privacy For Query Servers (Cpa)mentioning

confidence: 99%

A systematic overview on methods to protect sensitive data provided for various analyses

Templ

Sariyar

2022

Int. J. Inf. Secur.

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In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k-anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.

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Section: Differential Privacy For Query Servers (Cpa)mentioning

confidence: 99%

A systematic overview on methods to protect sensitive data provided for various analyses

Templ

Sariyar

2022

Int. J. Inf. Secur.

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“…A su misma vez, señala en el mismo informe que en el periodo 2030-2035 la propia noción de datos personales desaparecerá por la facilidad con la que las plataformas digitales podrán identificar -a partir de los datos que han ido recabando sobre ellos-a sus usuarios (Buttarelli, 2017). Incluso hoy en día, un estudio ya demuestra que es posible identificar al 87 % de la población de los Estados Unidos a partir de su código postal, fecha de nacimiento y sexo (Davis y Osoba, 2016), datos que nos podrían parecer a primera vista como «inofensivos».…”

Section: ¿Qué Es El Big Data?unclassified

Big data, privacidad y mercados digitales: los nuevos desafíos de la regulación en la UE

Estella¹,

Martínez²

2022

ceflegal

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Este trabajo ha obtenido un accésit del Premio «Estudios Financieros» 2021 en la modalidad de Derecho Constitucional y Administrativo. En fechas recientes el Parlamento Europeo ha emitido un informe favorable al proyecto de la Comisión Europea sobre la estrategia europea de datos. Este nuevo activo estratégico se ha convertido en un elemento esencial, no solo para la actuación de las empresas en el mercado, sino en la vida de los ciudadanos y su forma de relacionarse. En los últimos 15 años los mercados han experimentado vertiginosos cambios derivados, entre otras cosas, de la digitalización de nuevos modelos empresariales y formas de hacer negocio. Pese a que la Unión Europea aspira a la creación de un verdadero mercado único digital, lo cierto es que en la actualidad las empresas que dominan el panorama (Google, Facebook, Amazon y Apple) son todas estadounidenses. La ingente acumulación de datos personales que dichas plataformas digitales acumulan y aprovechan comercialmente –el big data– plantea indudables riesgos para la protección del derecho fundamental a la protección de los datos personales de los usuarios. En este trabajo se analiza de forma crítica el enfoque comunitario con el que se afronta este escenario, al hilo de operaciones de concentración empresarial o actuaciones de las autoridades de competencia contra dichos gigantes tecnológicos. Se prestará especial atención al contenido constitucional de este derecho, así como al posible solapamiento entre el derecho regulatorio y el administrativo sancionador, en concreto, en el ámbito del derecho de la competencia.

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“…This type of aggregate data mining by third parties can still be linked back to the individual. In 2000, Latanya Sweeney first demonstrated that 87% of the US population (216 million people) could be uniquely identified from only their data of birth, gender, and 5-digit zip code [96][97][98]. More recently, she demonstrated the ability to correctly identify 25% of research participants by name and 28% by address from data redacted beyond the HIPAA Safe Harbor standard [99].…”

Section: Themes Data Transmission and Storagementioning

confidence: 99%

“…More recently, she demonstrated the ability to correctly identify 25% of research participants by name and 28% by address from data redacted beyond the HIPAA Safe Harbor standard [99]. Other authors have demonstrated the ability to re-identify at least 90% of Americans utilizing credit card metadata or via statistical models [96,100,101]. Given this emerging area of research, the need to systemically identify all stakeholders and potential data "owners" becomes increasingly essential in the identification of potential downstream security risks to users.…”

Section: Themes Data Transmission and Storagementioning

confidence: 99%

Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019

Galvin

DeMuro²

2020

Yearb Med Inform

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Objectives: To survey international regulatory frameworks that serve to protect privacy of personal data as a human right as well as to review the literature regarding privacy protections and data ownership in mobile health (mHealth) technologies between January 1, 2016 and June 1, 2019 in order to identify common themes. Methods: We performed a review of relevant literature available in English published between January 1, 2016 and June 1, 2019 from databases including PubMed, Google Scholar, and Web of Science, as well as relevant legislative background material. Articles out of scope (as detailed below) were eliminated. We categorized the remaining pool of articles and discrete themes were identified, specifically: concerns around data transmission and storage, including data ownership and the ability to re-identify previously de-identified data; issues with user consent (including the availability of appropriate privacy policies) and access control; and the changing culture and variable global attitudes toward privacy of health data. Results: Recent literature demonstrates that the security of mHealth data storage and transmission remains of wide concern, and aggregated data that were previously considered “de-identified” have now been demonstrated to be re-identifiable. Consumer-informed consent may be lacking with regard to mHealth applications due to the absence of a privacy policy and/or to text that is too complex and lengthy for most users to comprehend. The literature surveyed emphasizes improved access control strategies. This survey also illustrates a wide variety of global user perceptions regarding health data privacy. Conclusion: The international regulatory framework that serves to protect privacy of personal data as a human right is diverse. Given the challenges legislators face to keep up with rapidly advancing technology, we introduce the concept of a “healthcare fiduciary” to serve the best interest of data subjects in the current environment.

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Privacy Preservation in the Age of Big Data: A Survey

Cited by 10 publications

References 18 publications

A systematic overview on methods to protect sensitive data provided for various analyses

A systematic overview on methods to protect sensitive data provided for various analyses

Big data, privacidad y mercados digitales: los nuevos desafíos de la regulación en la UE

Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019

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