Non-personal Data Collection for Toy User Interfaces

Toy-user-interfaces (ToyUI) are computing devices or peripherals that leverage interactivity and connectivity with other devices to promote physical and social play. ToyUI products may collect both personal and non-personal data (NPD) on their users. We propose nine data patterns for NPD collection as part of ToyUI design based on the study of 297 ToyUI items from both the literature and industry. In addition, we introduce a printed circuit board (PCB) used for rapid prototyping that enabled NPD data collection concerning both objects and users by gathering non-personal identification, positioning system, and motion tracking. We demonstrate the effectiveness of our hardware architecture by embedding it into two design scenarios, namely, closed rules and open-ended rules solutions. The objectives here are to assist the ToyUI makers in creating more meaningful play experiences while ensuring the privacy of children’s and their parents’ data

A Literature Survey on Smart Toy-related Children\u27s Privacy Risks

Smart toys have become popular as technological solutions offer a better experience for children. However, the technology used increases the risks to children\u27s privacy, which does not seem to have become a real concern for toy makers. Most researchers in this domain are vague in defining their motivations due to lack of an expert survey to support them. We conducted a literature survey to find papers on smart toy-related children\u27s privacy risks and mitigation solutions. We analyzed 26 papers using a taxonomy for privacy principles and preserving techniques adapted from the IoT context. Our analysis shows that some types of risks received more attention, especially (a) confidentiality, (b) use, retention and disclosure limitation, (c) authorization, (d) consent and choice, (e) openness, transparency and notice and (f) authentication. As for solutions, few were effectively presented; the vast majority related to data restriction -- (a) access control and (b) cryptographic

IoT4Fun Rapid Prototyping Toolkit for Smart Toys

Rapid prototyping tools turn the design of smart toys faster and easier for creative teams. Appropriate tools for smart toys should meet a list of requirements, which include distributed data collection and adaptability for assorted toy shapes and size. The IoT4Fun toolkit innovates by mixing the embedded, modular, and plug-and-play approaches. It supports motion tracking data, wireless communication, and contactless identification. IoT4Fun demonstrates its effectiveness to design a variety of smart toy solutions by fitting into a hula-hoop toy until spherical, cubic, and wearable shapes. Solutions connect with either mobile applications or other toys and play rules range from open-ended to closed behaviors. End-users exhaustively tested developed solutions, and technical assessment evaluates their integrity after playtesting sessions. Results show comparative data on battery consumption and vulnerabilities threats for data security and privacy of each design. Future versions of IoT4Fun can benefit from miniaturization, robustness, and reliability improvements

A Privacy-Preserving Context Ontology (PPCO) for Smart Connected Toys

© 2019 IEEE. Ubiquitous mobile technology like Smart Connected Toys (SCTs) have unique challenges of clearly defining context data elements due to unstructured, consistent, and persistent changes in the environment. SCTs interact with its context to achieve meaningful functionality while maintaining context data privacy. As SCTs become increasingly pervasive, the toys with their built-in features must be aware of and adapt to their changing contexts while providing a sense of privacy and security to contextual data processed to support its use. This paper presents a context profile through SCT Privacy-Preserving Context Ontology (PPCO) and examines the benefits of designing a context data model for SCT privacy goals. Our proposed data context model is an abstract model, which organizes elements of data and standardizes how they relate to one another. It organizes properties of related entries in SCT based on eXtensible Markup Language (XML) to depict and project how the SCT contextual information-related to the SCTs\u27 environment-is assembled and maintained. Ultimately, the PPCO provides a structured description of the SCT context profile necessary to identify needed privacy controls to support SCT privacy goals

A strategy action from the Cievs/Pernambuco in response to the emergency on Congenital Syndrome associated to Zika virus infection: an integrative action

Abstract Objectives: to describe the strategy action from the Centro de Informações Estratégicas de Vigilância em Saúde (Cievs/PE) (Strategic Information on Health Surveillance Center) in response to the emergency on Congenital Syndrome associated to Zika virus infection (CSZ) in Pernambuco State between 2015 and 2016. Methods: description performed on the strategies and activities developed by Cievs/PE during the important international public health emergency related to CSZ. Results: participated in detecting suspected CSZ cases; participated in elaborating clinical epidemiological protocols; developed electronic forms to notify CSZ cases and pregnant women with exanthema rashes; prepared epidemiological reports; developed a website about the emergency on the Cievs/PE website; insert the occurrence in the Comitê de Avaliação e Monitoramento de Eventos (CAME) (Committee to Assess and Monitor Occurrence); resolution of demands during readiness; technical visits from National and International institutions. The actions developed by the Cievs/PE were fundamental in detecting and following-up on 2,073 CSZ cases. 390 cases were confirmed (18.1%) and 1,413 were discarded (65.6%), and 4,467 pregnant women had exanthema rash. Conclusions: the action from the Cievs/PE allowed to employ timely strategies on preparation and response in a qualified and cooperative way to face public health emergency on CSZ'

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.