Browsing by Subject "privacy"

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  • Anttila, Juhani; Jussila, Kari Pauli (IEEE, 2018)
    Information security management needs to be considered from the perspective of individuals, organizations and the society as a whole. The current situation is not satisfactory with regard to the concepts or practices and is becoming more challenging in the future. Further research and development of the managerial methodologies and practices are necessary for the needs of the new business environments, SMEs and startups. This our research focuses on the comprehensive and multi-disciplinary framework that aims at providing challenges for the new assorted research initiatives and innovations, and insight and guidance for the implementers who integrate the information security solutions within the management of business systems and processes together with other specialized managerial viewpoints. At present, the studies and practical implementations are very scattered and separate from each other, and difficult to be reconciled. Also effective collaboration of the administrative authorities, business leaders and security specialists, and effective links between the managerial, human and technical viewpoints are emphasized.
  • Gaye, Amadou; Marcon, Yannick; Isaeva, Julia; LaFlamme, Philippe; Turner, Andrew; Jones, Elinor M.; Minion, Joel; Boyd, Andrew W.; Newby, Christopher J.; Nuotio, Marja-Liisa; Wilson, Rebecca; Butters, Oliver; Murtagh, Barnaby; Demir, Ipek; Doiron, Dany; Giepmans, Lisette; Wallace, Susan E.; Budin-Ljosne, Isabelle; Schmidt, Carsten Oliver; Boffetta, Paolo; Boniol, Mathieu; Bota, Maria; Carter, Kim W.; deKlerk, Nick; Dibben, Chris; Francis, Richard W.; Hiekkalinna, Tero; Hveem, Kristian; Kvaloy, Kirsti; Millar, Sean; Perry, Ivan J.; Peters, Annette; Phillips, Catherine M.; Popham, Frank; Raab, Gillian; Reischl, Eva; Sheehan, Nuala; Waldenberger, Melanie; Perola, Markus; van den Heuvel, Edwin; Macleod, John; Knoppers, Bartha M.; Stolk, Ronald P.; Fortier, Isabel; Harris, Jennifer R.; Woffenbuttel, Bruce H. R.; Murtagh, Madeleine J.; Ferretti, Vincent; Burton, Paul R. (2014)
  • Dhir, Amandeep; Torsheim, Torbjorn; Pallesen, Stale; Andreassen, Cecilie S. (2017)
    Selfies, or self-portraits, are often taken and shared on social media for online self-presentation reasons, which are considered essential for the psychosocial development and well-being of people in today's culture. Despite the growing popularity and widespread sharing of selfies in the online space, little is known about how privacy concerns moderate selfie behavior. In addition to this, it is also not known whether privacy concerns across age and gender groups influence selfie behavior. To address this timely issue, a survey assessing common selfie behaviors, that is, frequency of taking (individual and group selfies), editing (cropping and filtering), and posting selfies online, and social media privacy concerns (over personal data being accessed and misused by third parties) was conducted. The web-survey was administered to 3,763 Norwegian social media users, ranging from 13 to 50 years, with a preponderance of women (n = 2,509, 66.7%). The present study investigated the impact of privacy concerns on selfie behaviors across gender and age groups (adolescent, young adult, and adult) by use of the structural equation modeling approach. The results suggest that young adults have greater privacy concerns compared to adolescents and adults. Females have greater privacy concerns than males. Greater privacy concerns among female social media users were linked to lower engagement in selfie behavior, but privacy concerns did not influence selfie behavior in the case of male adolescents and young adults. Overall, privacy concerns were more consistently and inversely related to selfie behavior (taking and posting) among females than males. The study results have theoretical as well as practical implications for both researchers and policy makers.
  • Snell, Karoliina (2019)
    In Finland, as well as all over the globe, great weight is put on the possibilities of large data collections and ‘big data’ for generating economic growth, enhancing medical research, and boosting health and wellbeing in totally new ways. This massive data gathering and usage is justified by the moral principle of improving health. The imperative of health thus legitimizes data collection, new infrastructures and innovation policy. It is also supported by the rhetoric of health promotion. New arrangements in health research and innovations in the health sector are justified, as they produce health, while the moral principle of health also obligates individual persons to pursue healthy lifestyles and become healthy citizens. I examine how, in this context of Finnish data-driven medicine, arguments related to privacy and autonomy become silenced when contrasted with the moral principle of health.
  • Laakom, Firas; Raitoharju, Jenni; Nikkanen, Jarno; Iosifidis, Alexandros; Gabbouj, Moncef (IEEE, 2021)
    IEEE Access 9, 39560-39567
    In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.
  • Salokannel, Marjut; Tarkkala, Heta; Snell, Karoliina (2019)
    Biobank operations started officially in Finland in 2013 when the Biobank Act defining and regulating biobank operations came into force. Since then, ten biobanks have been established and they have started to collect new prospective samples with broad consent. The main corpus of biobank samples, however, consists of approximately 10 million “legacy samples”. These are old diagnostic or research samples that were transferred to biobanks in accordance with the Biobank Act. The focus of this article is on ambiguities concerning these legacy samples and their transfer in terms of legality, human rights, autonomy, and social sustainability. We analyse the Finnish biobank operations in the context of international regulation, such as the European Convention of Human Rights, the Oviedo Convention, European Charter of Fundamental Rights, the GDPR, and EU Clinical Trials Regulation, and show that the practice of using legacy samples is at times problematic in relation to this regulatory framework. We argue that the prevailing interpretations of these regulations as translated into the Finnish biobank practices undermine the autonomy of individuals by not giving individuals a right to consent or an actionable right to opt-out of the transfer of these legacy samples to the biobank. This is due to the fact that individuals are not given effective notification of such transfers. Thus, issues regarding the legal status of the biobank samples and the social sustainability of biobank operations remain a challenge for biobanks in Finland despite governmental efforts to create pioneering, comprehensive, and enabling legislation.
  • Pal, Ranjan; Crowcroft, Jon; Kumar, Abhishek; Hui, Pan; Haddadi, Hamed; De, Swades; Ng, Irene; Tarkoma, Sasu; Mortier, Richard (University of Cambridge, 2018)
    Technical Report
    In the era of the mobile apps and IoT, huge quantities of data about individuals and their activities offer a wave of opportunities for economic and societal value creation. However, the current personal data ecosystem is fragmented and inefficient. On one hand, end-users are not able to control access (either technologically, by policy, or psychologically) to their personal data which results in issues related to privacy, personal data ownership, transparency, and value distribution. On the other hand, this puts the burden of managing and protecting user data on apps and ad-driven entities (e.g., an ad-network) at a cost of trust and regulatory accountability. In such a context, data holders (e.g., apps) may take advantage of the individuals’ inability to fully comprehend and anticipate the potential uses of their private information with detrimental effects for aggregate social welfare. In this paper, we investigate the problem of the existence and design of efficient ecosystems (modeled as markets in this paper) that aim to achieve a maximum social welfare state among competing data holders by preserving the heterogeneous privacy preservation constraints up to certain compromise levels, induced by their clients, and at the same time satisfying requirements of agencies (e.g., advertisers) that collect and trade client data for the purpose of targeted advertising, assuming the potential practical inevitability of some amount inappropriate data leakage on behalf of the data holders. Using concepts from supply-function economics, we propose the first mathematically rigorous and provably optimal privacy market design paradigm that always results in unique equilibrium (i.e, stable) market states that can be either economically efficient or inefficient, depending on whether privacy trading markets are monopolistic or oligopolistic in nature. Subsequently, we characterize in closed form, the efficiency gap (if any) at market equilibrium.
  • Chen, Liang; Thombre, Sarang; Järvinen, Kimmo; Lohan, Elena Simona; Alén-Savikko, Anette; Leppäkoski, Helena; Bhuiyan, M. Zahidul H.; Bu-Pasha, Shakila; Ferrara, Giorgia Nunzia; Honkala, Salomon; Lindqvist, Jenna; Ruotsalainen, Laura; Korpisaari, Päivi; Kuusniemi, Heidi (2017)
    Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.
  • Moen, Pirjo; Ruohomaa, Sini Susanna; Viljanen, Lea Anneli; Kutvonen, Lea (University of Helsinki, Department of Computer Science, 2010)
    Department of Computer Science Series of Publications C
    Inter-enterprise collaboration has become essential for the success of enterprises. As competition increasingly takes place between supply chains and networks of enterprises, there is a strategic business need to participate in multiple collaborations simultaneously. Collaborations based on an open market of autonomous actors set special requirements for computing facilities supporting the setup and management of these business networks of enterprises. Currently, the safeguards against privacy threats in collaborations crossing organizational borders are both insufficient and incompatible to the open market. A broader understanding is needed of the architecture of defense structures, and privacy threats must be detected not only on the level of a private person or enterprise, but on the community and ecosystem levels as well. Control measures must be automated wherever possible in order to keep the cost and effort of collaboration management reasonable. This article contributes to the understanding of the modern inter-enterprise collaboration environment and privacy threats in it, and presents the automated control measures required to ensure that actors in inter-enterprise collaborations behave correctly to preserve privacy.
  • Kumar, Abhishek; Finley, Benjamin John; Braud, Tristan; Tarkoma, Sasu; Hui, Pan (2021)
    Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.
  • Tani, Antti (Helsingin yliopisto, 2020)
    The release of Bitcoin marked the birth of blockchain applications. Due, among other things, to the need for public verifiability, blockchain information is often transparent, which in many cases leads to insufficient privacy. Various methods have been developed to obfuscate the blockchain data, which should at the same time maintain public verifiability. A promising cryptographic approach is zero-knowledge proof that enables a statement to be proved without revealing any other information than the validity of the statement. Zero-knowledge proofs are examined in detail, first focusing on their general properties. With blockchains, the key features for zero-knowledge proof schemes are non-interactivity and succinctness, and schemes that fulfill these requirements are often called as zk-SNARKs. In a limited use, where succinctness is not critical, Fiat-Shamir transform has also been useful. We study the use of zero-knowledge proofs in blockchain applications Zcash, Ethereum and Monero, with a particular focus on privacy and feasibility.