Big data privacy: The datafication of personal information

Mai, Jens‐Erik

doi:10.1080/01972243.2016.1153010

Cited by 140 publications

(84 citation statements)

References 10 publications

(10 reference statements)

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“…The retail store, Target, has been cited for utilizing big data to gather information about individuals' purchasing behaviors to send them coupons. Target's algorithm for using data uses predictive analytics and has been able to determine whether a shopper is pregnant based on previous purchasing behaviors of the last 25 items , . This predictive data has the potential to make false inferences or to alert members of this person's household that she is pregnant before she has had the chance to do so herself.…”

Section: Big Data Definedmentioning

confidence: 99%

“…This section explores two ways that library information professionals can address privacy concerns with big data. It explores the datafication model and resource description and access (RDA), as well as linked data technologies . When using big data to interact with a system, rather than individuals, privacy issues can be mitigated.…”

Section: Addressing Privacy‐related Concerns With Big Datamentioning

confidence: 99%

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Big Data's Impact on Privacy for Librarians and Information Professionals

Harper

Oltmann

2017

Bull of the Asso Info Sci

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EDITOR'S SUMMARY In a digital age, it is very difficult to maintain complete privacy when posting on social media or making purchases. Individual activity on the internet is increasingly collected by corporations, even with the user's knowledge, and can be used to predict future behavior, purchasing choices or other sensitive subjects. This data analysis is often done without a user's consent and in many cases presents unethical behavior and breaches of privacy. In the world of libraries, the privacy of patrons has been tantamount for decades, but trying to keep up with privacy codes and still make use of this big data can be challenging for librarians. Big data can be beneficial to libraries in many ways, and if pointed at library systems, rather than the habits of patrons, can also keep privacy intact.

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Section: Big Data Definedmentioning

confidence: 99%

Section: Addressing Privacy‐related Concerns With Big Datamentioning

confidence: 99%

Big Data's Impact on Privacy for Librarians and Information Professionals

Harper

Oltmann

2017

Bull of the Asso Info Sci

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“…In recent years, due to the widespread use of the Internet of Things and big data technologies, the amount of personal information collected by some large internet companies and social networking services has reached an unprecedented level [1]. These personal data are very valuable for the public and private sectors to improve their products or services.…”

Section: Introductionmentioning

confidence: 99%

Personal Data Market Optimization Pricing Model Based on Privacy Level

Yang

Xing

2019

Information

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In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges:(1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer's attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers' willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform.

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“…The addition of data analytics techniques transforms acquired data into raw material for predictions about human behavior and supports automated decisions and interventions. Mai (2016) describes these practices as "datafication," a dimension of surveillance in which machine learning and other algorithmic techniques surface patterns from prior events that correlate to outcomes demonstrated by others, yielding new knowledge about a person, place, or group. Growing evidence indicates that decision-making based on datafied surveillance reproduces and amplifies the structural inequalities reflected in the underlying datasets employed (Barocas and Selbst 2016;Crawford 2016), which could have multiple adverse impacts for data subjects long term (Katell 2018).…”

Section: Introductionmentioning

confidence: 99%

Municipal Surveillance Regulation and Algorithmic Accountability

2019

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A wave of recent scholarship has warned about the potential for discriminatory harms of algorithmic systems, spurring an interest in algorithmic accountability and regulation. Meanwhile, parallel concerns about surveillance practices have already led to multiple successful regulatory efforts of surveillance technologies-many of which have algorithmic components. Here, we examine municipal surveillance regulation as offering lessons for algorithmic oversight. Taking the 2017 Seattle Surveillance Ordinance as our primary case study and surveying efforts across five other cities, we describe the features of existing surveillance regulation; including procedures for describing surveillance technologies in detail, processes for public engagement, and processes for establishing acceptable uses. Although these surveillance-focused laws were not intended to address algorithmic accountability, we find these considerations to be relevant to the law's aim of surfacing disparate impacts of systems in use. We also find that in notable cases, government employees did not identify regulated algorithmic surveillance technologies as reliant on algorithmic or machine learning systems, highlighting a definitional gap that could hinder future efforts toward algorithmic regulation. We argue that (i) finer-grained distinctions between types of analytic and information systems in the language of law and policy, and (ii) risk assessment tools integrated into their implementation would both strengthen future regulatory efforts by rendering underlying algorithmic components more legible and accountable to political and community stakeholders.

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Big data privacy: The datafication of personal information

Cited by 140 publications

References 10 publications

Big Data's Impact on Privacy for Librarians and Information Professionals

Big Data's Impact on Privacy for Librarians and Information Professionals

Personal Data Market Optimization Pricing Model Based on Privacy Level

Municipal Surveillance Regulation and Algorithmic Accountability

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