Cooperative Privacy-Preserving Data Collection Protocol Based on Delocalized-Record Chains

This paper aims to advance the field of data anonymization within the context of Internet of Things (IoT), an environment where data collected may contain sensitive information about users. Specifically, we propose a privacy-preserving data publishing alternative that extends the privacy requirement to the data collection phase. Because our proposal offers privacy-preserving conditions in both the data collecting and publishing, it is suitable for scenarios where a central node collects personal data supplied by a set of devices, typically associated with individuals, without these having to assume trust in the collector. In particular, to limit the risk of individuals' re-identification, the probabilistic k-anonymity property is satisfied during the data collection process and the k-anonymity property is satisfied by the data set derived from the anonymization process. To carry out the anonymous sending of personal data during the collection process, we introduce the delocalized-record chain, a new mechanism of anonymous communication aimed at multi-user environments to collaboratively protect information, which by not requiring third-party intermediaries makes it especially suitable for private IoT networks (besides public IoT networks)

Incremental k-Anonymous microaggregation in large-scale electronic surveys with optimized scheduling

Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. k-Anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k–1 individuals, through the aggregation of demographic attributes, otherwise a potential culprit for respondent reidentification. Although privacy guarantees are by no means absolute, the elegant simplicity of the k-anonymity criterion and the excellent preservation of information utility of microaggregation algorithms has turned them into widely popular approaches whenever data utility is critical. Unfortunately, high-utility algorithms on large datasets inherently require extensive computation. This work addresses the need of running k-anonymous microaggregation efficiently with mild distortion loss, exploiting the fact that the data may arrive over an extended period of time. Specifically, we propose to split the original dataset into two portions that will be processed subsequently, allowing the first process to start before the entire dataset is received, while leveraging the superlinearity of the microaggregation algorithms involved. A detailed mathematical formulation enables us to calculate the optimal time for the fastest anonymization, as well as for minimum distortion under a given deadline. Two incremental microaggregation algorithms are devised, for which extensive experimentation is reported. The theoretical methodology presented should prove invaluable in numerous data-collection applications, including largescale electronic surveys in which computation is possible as the data comes in.Peer ReviewedPostprint (published version

Organic sulfur: a spatially variable and understudied component of marine organic matter

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Longnecker, K., Oswald, L., Soule, M. C. K., Cutter, G. A., & Kujawinski, E. B. Organic sulfur: a spatially variable and understudied component of marine organic matter. Limnology and Oceanography Letters, (2020), doi:10.1002/lol2.10149.Sulfur (S) is a major heteroatom in organic matter. This project evaluated spatial variability in the concentration and molecular‐level composition of organic sulfur along gradients of depth and latitude. We measured the concentration of total organic sulfur (TOS) directly from whole seawater. Our data reveal high variability in organic sulfur, relative to established variability in total organic carbon or nitrogen. The deep ocean contained significant amounts of organic sulfur, and the concentration of TOS in North Atlantic Deep Water (NADW) decreased with increasing age while total organic carbon remained stable. Analysis of dissolved organic matter extracts by ultrahigh resolution mass spectrometry revealed that 6% of elemental formulas contained sulfur. The sulfur‐containing compounds were structurally diverse, and showed higher numbers of sulfur‐containing elemental formulas as NADW moved southward. These measurements of organic sulfur in seawater provide the foundation needed to define the factors controlling organic sulfur in the global ocean.We thank Catherine Carmichael, Winifred Johnson, and Gretchen Swarr for assistance with sample collection and processing, and Joe Jennings for the analysis of inorganic nutrients. The help of the captain and crew of the R/V Knorr and the other cruise participants during the “DeepDOM” cruise is appreciated. Two anonymous reviewers and Patricia Soranno provided thorough comments that greatly improved the manuscript. The ultrahigh resolution mass spectrometry samples were analyzed at the WHOI FT‐MS Users' Facility that is funded by the National Science Foundation (grant OCE‐0619608) and the Gordon and Betty Moore Foundation (GMBF1214). This project was funded by NSF grants OCE‐1154320 (to EBK and KL), the W.M. Marquet Award (to KL), and OCE‐1435708 (to GAC). The authors declare no conflicts of interest

Ascertaining the Impact of P–12 Engineering Education Initiatives: Student Impact through Teacher Impact

The widespread need to address both science, technology, engineering, and math (STEM) education and STEM workforce development is persistent. Underscored by the Next Generation Science Standards, demand is high for P–12 engineering-centered curricula. TeachEngineering is a free, standards-aligned NSF-funded digital library of more than 1,500 hands-on, design-rich K–12 engineering lessons and activities. Beyond anonymous site-user counts, the impact of the TeachEngineering collection and outreach initiatives on the education of children and their teachers was previously unknown. Thus, the project team wrestled with the question of how to meaningfully ascertain classroom impacts of the digital engineering education library and—more broadly—how to ascertain the impacts of teacher-focused P–12 engineering education initiatives. In this paper, the authors approach the classroom impact question through probing self-reported differentials in: (1) teachers’ confidence in teaching engineering concepts, and (2) changes in their teaching practices as a result of exposure to (and experiences with) K–12 engineering education resources and outreach opportunities. In 2016, four quantitative and qualitative surveys were implemented to probe the impact of the TeachEngineering digital library and outreach on four populations of K–12 teachers’ confidence and practices, including the frequency with which they integrate engineering into their precollege classrooms. Survey results document the teacher experience and perception of using hands-on K–12 engineering curricular materials in the classroom and help create a data-driven understanding of where to best invest future resources. The results suggest that the TeachEngineering curricular resources and outreach initiatives help teachers build confidence in their use of engineering curriculum and pedagogy in K–12 classrooms, impact their teaching practices, and increase their likelihood of teaching engineering in the classroom in the future

Privacy Preservation by Disassociation

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniquesa) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.Comment: VLDB201

Does Twitter Create Similar Patterns of Positivity/Negativity as Face-to-Face Word-of-Mouth?

Word-of-mouth communication is important to organizations because it is a free form of advertising and has been shown to be influential on consumers’ purchasing decisions. Marketers of course, would like WOM to be positive and thus increase brand reputation and sales. In the past decade, new forms of communication have created different channels for WOM to travel through. Current social media websites such as Facebook and Twitter allow one person to send a message to many almost instantaneously. This study’s objective is to examine the WOM on the networking site Twitter. Previous research has indentified the relative incidence rates of both positive and negative recommendations for face-to-face WOM, but the anonymity of Twitter may result in different rates. Looking at recent box office movies, over 2,000 posts, commonly called “tweets” were collected to examine the valence. The results were unexpected. Every movie examined, despite how critics reviewed them, received overwhelmingly positive results, with the average close to a 9 to 1 ratio