The Death of Privacy?

Froomkin, A. Michael

doi:10.2307/1229519

Cited by 151 publications

(61 citation statements)

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“…Conventionally, identifying attributes such as name or social security number are not disclosed, in order to protect privacy. Still, recent research [Froomkin 2000;Sweeney 2002] has demonstrated that this is not sufficient, due to the existence of quasi-identifiers in the released microdata. Quasi-identifiers are sets of attributes (e.g., ZIP, Gender, DateOfBirth ) which can be joined with information obtained from diverse sources (e.g., public voting registration data) in order to reveal the identity of individual records.…”

Section: Introductionmentioning

confidence: 99%

A framework for efficient data anonymization under privacy and accuracy constraints

Ghinita

Karras

Kalnis

et al. 2009

ACM Trans. Database Syst.

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Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy-preserving paradigms of k-anonymity and -diversity. k-anonymity protects against the identification of an individual's record. -diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) -diversification is solved by techniques developed for the simpler k-anonymization problem, causing unnecessary information loss.(ii) The anonymization process is inefficient in terms of computational and I/O cost. (iii) Previous research focused exclusively on the privacy-constrained problem and ignored the equally important accuracy-constrained (or dual) anonymization problem.In this article, we propose a framework for efficient anonymization of microdata that addresses these deficiencies. First, we focus on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and -diversity models for the privacy-constrained (i.e., direct) and the accuracy-constrained (i.e., dual) anonymization problems. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multidimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the existing approaches in terms of execution time and information loss.

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Section: Introductionmentioning

confidence: 99%

A framework for efficient data anonymization under privacy and accuracy constraints

Ghinita

Karras

Kalnis

et al. 2009

ACM Trans. Database Syst.

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“…It is now practical-even cheap-for organizations to track Internet usage, sites visited, and software opened; to monitor e-mail communications; to log keystrokes, cookies, clicktrails, and improper distribution of intellectual property (using "snitchware"); to listen in on telephone conversations and meetings; to screen caller IDs; to conduct video surveillance, including the use of recognition technologies to determine gender, age, and even identity on lowresolution security monitor footage; to monitor location through GPS software embedded in mobile devices or vehicles; to track who is meeting with whom using mobile phone sensors; and to conduct sense-enhanced searches which look through clothes or skin to uncover hidden threats, anxiety, or even mood/emotion (Froomkin, 2000;Levinson, 2009;Smith-Butler, 2009). …”

Section: Observation As a Technologymentioning

confidence: 99%

Making Transparency Transparent: The Evolution of Observation in Management Theory

Bernstein¹

2017

ANNALS

169

144

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Observation is key to management scholarship and practice. Yet a holistic view of its role in management has been elusive, in part due to shifting terminology. The current popularity of the term "transparency" provides the occasion for a thorough review, which finds (a) a shift in the object of observation from organizational outcomes to the detailed individual activities within them; (b) a shift from people observing the technology to technology observing people; and (c) a split in the field, with managers viewing observation almost entirely from the observer's perspective, leaving the perspective of the observed to the realm of scholarly methodology courses and philosophical debates on privacy. I suggest how the literature on transparency and related literatures might be improved with research designed in light of these trends.KEYWORDS: transparency, privacy, performance, organizations, management theory ACKNOWLEDGEMENTS: I thank Julie Battilana, Ethan Burris, Tim Earle, Amy Edmondson, John Elder, Elizabeth Hansen, Rosabeth Moss Kanter, Joshua Margolis, Tsedal Neeley, Nitin Nohria, Shefali Patil, Kathy Qu, Ryan Raffaelli, Lakshmi Ramarajan, and Bradley Staats for feedback on prior versions of this work; Jim Detert, Sim Sitkin, and two anonymous reviewers for insightful and developmental feedback; and the Harvard Business School for financial support.Please cite as: Bernstein, E. S. Making transparency transparent: The evolution of observation in management theory. Academy of Management Annals, 11(1): 217-266. 2We are increasingly observed and observing at work. Fifty years ago, a typical manager might have tracked production, revenue, and expenses against budget and periodically observed workers during in-person audits (e.g., Dalton, 1959). Today, advances in technology, from smart cameras to wearable tracking devices, make possible a kind of real-time "SuperVision" (Gilliom & Monahan, 2012) far beyond any level of observability envisioned 50 years ago or when Frederick Taylor (1911) originally promoted managerial oversight through scientific management.Public attention is captured by extreme examples of observation at work, like handheld computers (Amazon) or wearable bands (Tesco) tracking and optimizing employees' every move (Head, 2014;Kantor & Streitfeld, 2015;Rawlinson, 2013), embedded sensors in large fleets of company-owned trucks (e.g., UPS) recording hundreds of measurements to capture every action of the truck and its driver to unearth and enforce time-saving tactics (Goldstein, 2014;Levy, 2015), cameras at Las Vegas casino Harrah's tracking the smiles of card dealers and wait staff as a proxy for customer service quality (Peck, 2013), point-of-sale systems scraping every transaction for signs of employee fraud (Pierce, Snow, & McAfee, 2015), and RFID (radiofrequency identification)-enabled workspaces automatically capturing factory worker progress (Ranganathan, 2015), how long employees spend at their desks (Zillman, 2016), and even who does and does not use hand-soap and hand-sanitizer dispensers ...

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“…Another, by contrast, refers to our online manifestations as "second selves" or "digital doppelgängers" (Andrews, 2012, p. 45), again insisting on the belatedness and inferiority of our online presence when compared to our "real" material selves. e response to this perceived crisis is to call for more privacy safeguards to be built into search engines, online financial transactions, and the uses of data about individuals by the entities with which we interact (e.g., Pentland, 2009;Froomkin, 2000). More "privacy by design" is needed because "[p]rivacy is not something that appear[s] naturally online, it must be deliberately architected" (Castelluccia, 2012, p. 31; see also Lessig, 2013;Witte, 2013).…”

Section: Introductionmentioning

confidence: 99%

"All data is credit data"; or, on Close Reading as a Reciprocal Process in Digital Knowledge Environments

Hunter

2014

SRC

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The Death of Privacy?

Cited by 151 publications

References 0 publications

A framework for efficient data anonymization under privacy and accuracy constraints

A framework for efficient data anonymization under privacy and accuracy constraints

Making Transparency Transparent: The Evolution of Observation in Management Theory

"All data is credit data"; or, on Close Reading as a Reciprocal Process in Digital Knowledge Environments

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