Alexandros Mittos, Bradley Malin and Emiliano De Cristofaro
Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy experts.
Apostolos Pyrgelis, Carmela Troncoso and Emiliano De Cristofaro
Information about people’s movements and the locations they visit enables an increasing number of mobility analytics applications, e.g., in the context of urban and transportation planning, In this setting, rather than collecting or sharing raw data, entities often use aggregation as a privacy protection mechanism, aiming to hide individual users’ location traces. Furthermore, to bound information leakage from the aggregates, they can perturb the input of the aggregation or its output to ensure that these are differentially private.
In this paper, we set to evaluate the impact of releasing aggregate location time-series on the privacy of individuals contributing to the aggregation. We introduce a framework allowing us to reason about privacy against an adversary attempting to predict users’ locations or recover their mobility patterns. We formalize these attacks as inference problems, and discuss a few strategies to model the adversary’s prior knowledge based on the information she may have access to. We then use the framework to quantify the privacy loss stemming from aggregate location data, with and without the protection of differential privacy, using two real-world mobility datasets. We find that aggregates do leak information about individuals’ punctual locations and mobility profiles. The density of the observations, as well as timing, play important roles, e.g., regular patterns during peak hours are better protected than sporadic movements. Finally, our evaluation shows that both output and input perturbation offer little additional protection, unless they introduce large amounts of noise ultimately destroying the utility of the data.
Mohammad Etemad, Alptekin Küpçü, Charalampos Papamanthou and David Evans
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Damien Desfontaines, Andreas Lochbihler and David Basin
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important efforts have been made to streamline text mining workflows by providing a library of natural language processing (NLP) tools (e.g., stemmers, parsers, and named entity recognizers) that can be connected together in a pipeline Manning, Surdeanu, Bauer, Finkel, Bethard, McClosky, D., 2014 ; Savova, Masanz, Ogren, Zheng, Sohn, Kipper-Schuler, Chute, 2010 ; Batista-Navarro, Carter, Ananiadou, 2016 ; Clarke, Srikumar, Sammons, Roth, 2012 ). In addition, there are valuable machine learning packages that provide machine learning algorithms in a user-friendly manner
that have been included in at least 3,000 publications. The keywords are sorted in descending order. “Embryonic stem-cells” has 2.52 YK and “innate human immunity” has 1.57 YK.
Best 30 performers in terms of YK.
embryonic stem-cells; carbon nanotubes; field-effect transistors; graphite; genome-wide association; caenorhabditis-elegans; DNA methylation; living cells; regulatory t-cells; gold nanoparticles; tgf-beta; one-pot synthesis; quantum dots; functionalization; electrodes; acute myeloid-leukemia; long-term potentiation; activated
Minghong Chen, Jingye Qu, Yuan Xu and Jiangping Chen
, information extraction, and summarization requires understanding of the meaning of the texts, and has been challenging.
This study applies three types of text analysis/processing: (1) low-level natural language processing such as stop-word identification and filtering and stemming. The result helps to create a high quality word cloud that reveals the most frequent content words from the abstracts of the projects; (2) descriptive or bibliometric analysis. This is possible because the records of these NSF projects are well-organized datasets, as described in Table 1