Journal articles: 'Personal data control'

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Hodson, Hal. "Gatekeeper keeps your personal data under your control." New Scientist 220, no. 2937 (October 2013): 20–21. http://dx.doi.org/10.1016/s0262-4079(13)62382-9.

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Priisalu, Jaan, and Rain Ottis. "Personal control of privacy and data: Estonian experience." Health and Technology 7, no. 4 (June 15, 2017): 441–51. http://dx.doi.org/10.1007/s12553-017-0195-1.

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Ceccato, Natalie, and Courtney Price. "When personal health data is no longer “personal”." Healthcare Management Forum 32, no. 6 (August 1, 2019): 326–28. http://dx.doi.org/10.1177/0840470419865851.

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Enacted in 2000, the Canadian Personal Health Information Protection and Electronics Documents Act is an important piece of legislation aimed at safeguarding an individual’s right to control their personal health information. Since this time, the world of data and analytics has shifted in terms of our potential to collect, integrate, and analyze both structured and unstructured data. The implications for these data advancements are endless for our healthcare system; however, challenges influenced by our approach to collecting, accessing, and analyzing data as well as patient consent to share personal health information mean public entities lag behind commercial players in harnessing these potential benefits. While there are examples of data analytics application successes, Canadian healthcare continues to lag behind other countries and commercial sectors. We are at a pivot point for system improvements requiring a collective approach to collection, storage, linkage, and application of personal healthcare data. In the chasm of this rests how we address patient consent. All health leaders can play a central role in advancing our application of data for system improvements. Strategies to support health leaders in achieving this potential are outlined in this article.

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Ruggles, Myles. "Mixed Signals: Personal Data Control in the Intelligent Network." Media Information Australia 67, no. 1 (February 1993): 28–39. http://dx.doi.org/10.1177/1329878x9306700105.

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Whitley, Edgar A. "Informational privacy, consent and the “control” of personal data." Information Security Technical Report 14, no. 3 (August 2009): 154–59. http://dx.doi.org/10.1016/j.istr.2009.10.001.

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Pangrazio, Luci, and Neil Selwyn. "‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data." New Media & Society 21, no. 2 (September 20, 2018): 419–37. http://dx.doi.org/10.1177/1461444818799523.

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The capacity to understand and control one’s personal data is now a crucial part of living in contemporary society. In this sense, traditional concerns over supporting the development of ‘digital literacy’ are now being usurped by concerns over citizens’ ‘data literacies’. In contrast to recent data safety and data science approaches, this article argues for a more critical form of ‘personal data literacies’ where digital data are understood as socially situated and context dependent. Drawing on the critical literacies tradition, the article outlines a range of salient socio-technical understandings of personal data generation and processing. Specifically, the article proposes a framework of ‘Personal Data Literacies’ that distinguishes five significant domains: (1) Data Identification, (2) Data Understandings, (3) Data Reflexivity, (4) Data Uses, and (5) Data Tactics. The article concludes by outlining the implications of this framework for future education and research around the area of individuals’ understandings of personal data.

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Dadalko, V. A., and E. A. Timofeev. "Fundamentals of setting the compliance control of personal data protection." National Interests: Priorities and Security 16, no. 2 (February 14, 2020): 339–50. http://dx.doi.org/10.24891/ni.16.2.339.

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Ferrandu, Giovanni. "Control and protection tools of personal data in digital healthcare." Pharmaceuticals Policy and Law 19, no. 3-4 (October 17, 2018): 209–18. http://dx.doi.org/10.3233/ppl-180457.

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Heaven, Douglas. "Personal clouds let you take control of your own data." New Scientist 218, no. 2919 (June 2013): 22. http://dx.doi.org/10.1016/s0262-4079(13)61363-9.

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Ragesh, G. K., and K. Baskaran. "Cryptographically Enforced Data Access Control in Personal Health Record Systems." Procedia Technology 25 (2016): 473–80. http://dx.doi.org/10.1016/j.protcy.2016.08.134.

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Squires, Janet E., Alison M. Hutchinson, Anne-Marie Bostrom, Kelly Deis, Peter G. Norton, Greta G. Cummings, and Carole A. Estabrooks. "A Data Quality Control Program for Computer-Assisted Personal Interviews." Nursing Research and Practice 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/303816.

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Researchers strive to optimize data quality in order to ensure that study findings are valid and reliable. In this paper, we describe a data quality control program designed to maximize quality of survey data collected using computer-assisted personal interviews. The quality control program comprised three phases: (1) software development, (2) an interviewer quality control protocol, and (3) a data cleaning and processing protocol. To illustrate the value of the program, we assess its use in the Translating Research in Elder Care Study. We utilize data collected annually for two years from computer-assisted personal interviews with 3004 healthcare aides. Data quality was assessed using both survey and process data. Missing data and data errors were minimal. Mean and median values and standard deviations were within acceptable limits. Process data indicated that in only 3.4% and 4.0% of cases was the interviewer unable to conduct interviews in accordance with the details of the program. Interviewers’ perceptions of interview quality also significantly improved between Years 1 and 2. While this data quality control program was demanding in terms of time and resources, we found that the benefits clearly outweighed the effort required to achieve high-quality data.

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Nakakuki, Kazuo, and Shin-Ichi Kuribayashi. "Protocol and network control technology for personal mobile data communication." Electronics and Communications in Japan (Part I: Communications) 79, no. 10 (1996): 24–34. http://dx.doi.org/10.1002/ecja.4410791004.

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Prince, Christine. "Do consumers want to control their personal data? Empirical evidence." International Journal of Human-Computer Studies 110 (February 2018): 21–32. http://dx.doi.org/10.1016/j.ijhcs.2017.10.003.

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Santosa, Iqbal, and Raras Yusvinindya. "Risk Analysis and Control of Personal Data Protection in the Population Administration Information System." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 3 (December 11, 2019): 496–504. http://dx.doi.org/10.29207/resti.v3i3.1068.

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Sistem Informasi Administrasi Kependudukan (SIAK) is an application used in managing personal data of residents in all cities/districts in Indonesia. Personal data becomes the public attention because if it is not managed properly it will have an impact on one's legal protection and non-compliance with regulations, i.e. Permenkominfo Nomor 20 tahun 2016 about Protection of Personal Data in the Electronic System. Risk analysis and control of personal data protection on SIAK applications are needed so that the personal data management can be carried out properly and comply with regulatory requirements. Data collected for this study are primary data, sourced from direct observations on the application, interview about assets related to SIAK along with possible risks, and also internal organizations documents. Data analysis was performed with a risk analysis using the ISO 31000: 2018 risk management process approach, where the identification of relevant risks refers to the Generic Risk Scenarios COBIT 5 For Risk, and the determination of relevant controls refers to the Department of Defense Instruction 8500.2 and NIST 800-53. This research involves the Head of Department and employees of Disdukcapil XYZ City that are related to the strategic and operational aspects of SIAK. The results of this study are the identification of 23 possible risks that are spread over 5 processes of personal data protection that classified into the medium-high risk level, and proposed risk control consisting of 19 preventive controls, 6 detective controls, and 2 corrective control.

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Pitcher, Brian L., and Sung Young Hong. "Older Men's Perceptions of Personal Control." Sociological Perspectives 29, no. 3 (July 1986): 397–419. http://dx.doi.org/10.2307/1389027.

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Data from the Older Male Cohort of the National Longitudinal Surveys are analyzed in this study to investigate antecedents of perceptions of personal control. Particular attention is given to the effects of individual health factors. The findings support the life stress model that assumes that social conditions and environmental changes affect individual perceptions of control via opportunities and resources for carrying out efficacious actions. The analysis suggests, however, that various untested moderating conditions determine the nature and direction of the impact. Future investigations are needed to identify these moderating variables and specify their differential effects.

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Zheng, Yinhe, Rongsheng Zhang, Minlie Huang, and Xiaoxi Mao. "A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9693–700. http://dx.doi.org/10.1609/aaai.v34i05.6518.

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Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.

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Birch, Kean, DT Cochrane, and Callum Ward. "Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech." Big Data & Society 8, no. 1 (January 2021): 205395172110173. http://dx.doi.org/10.1177/20539517211017308.

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Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called “Big Tech” firms, consisting of Apple, Microsoft, Amazon, Google/Alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation—or “techlash”—of their market power based on their monopolistic control of personal data. We analyse the transformation of personal data into an asset in order to explore how personal data is accounted for, governed, and valued by Big Tech firms and other political-economic actors (e.g., investors). However, our findings show that Big Tech firms turn “users” and “user engagement” into assets through the performative measurement, governance, and valuation of user metrics (e.g., user numbers, user engagement), rather than extending ownership and control rights over personal data per se. We conceptualize this strategy as a form of “techcraft” to center attention on the means and mechanisms that Big Tech firms deploy to make users and user data measurable and legible as future revenue streams.

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Chaudhry, Amir, Jon Crowcroft, Heidi Howard, Anil Madhavapeddy, Richard Mortier, Hamed Haddadi, and Derek McAuley. "Personal Data: Thinking Inside the Box." Aarhus Series on Human Centered Computing 1, no. 1 (October 5, 2015): 4. http://dx.doi.org/10.7146/aahcc.v1i1.21312.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>We are in a ‘personal data gold rush’ driven by advertising being the primary revenue source for most online companies. These companies accumulate extensive personal data about individuals with minimal concern for us, the subjects of this process. This can cause many harms: privacy infringement, personal and professional embarrassment, restricted access to labour markets, restricted access to highest value pricing, and many others. There is a critical need to provide technologies that enable alternative practices, so that individuals can par- ticipate in the collection, management and consumption of their personal data. In this paper we discuss the Databox, a personal networked device (and associated services) that col- lates and mediates access to personal data, allowing us to re- cover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data. </span></p></div></div></div>

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Spiekermann, Sarah, and Jana Korunovska. "Towards a value theory for personal data." Journal of Information Technology 32, no. 1 (March 2017): 62–84. http://dx.doi.org/10.1057/jit.2016.4.

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Analysts, investors and entrepreneurs have recognized the value of personal data for Internet economics. Personal data is viewed as ‘the oil’ of the digital economy. Yet, ordinary people are barely aware of this. Marketers collect personal data at minimal cost in exchange for free services. But will this be possible in the long term, especially in the face of privacy concerns? Little is known about how users really value their personal data. In this paper, we build a user-centered value theory for personal data. On the basis of a survey experiment with 1269 Facebook users, we identify core constructs that drive the value of volunteered personal data. We find that privacy concerns are less influential than expected and influence data value mainly when people become aware of data markets. In fact, the consciousness of data being a tradable asset is the single most influential factor driving willingness-to-pay for data. Furthermore, we find that people build a sense of psychological ownership for their data and hence value it more. Finally, our value theory helps to unveil market design mechanisms that will influence how personal data markets thrive: First, we observe a majority of users become reactant if they are consciously deprived of control over their personal data; many drop out of the market. We therefore advice companies to consider user-centered data control tools to have them participate in personal data markets. Second, we find that in order to create scarcity in the market, centralized IT architectures (reducing multiple data copies) may be beneficial.

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McCullagh, Karen. "Protecting ‘privacy’ through control of ‘personal’ data processing: A flawed approach." International Review of Law, Computers & Technology 23, no. 1-2 (July 2009): 13–24. http://dx.doi.org/10.1080/13600860902742562.

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Shebunova, Ekaterina. "The issues of implementing legislation on personal data in the era of big data." Current Issues of the State and Law, no. 15 (2020): 319–26. http://dx.doi.org/10.20310/2587-9340-2020-4-15-319-326.

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We consider the impact of automation processes on the implementation of external financial control. We study the practical application features of new sources of data analysis – state information systems. In particular, the legal regulation of the functioning of such systems and their use for financial control purposes. We present methods for collecting and analyzing big data in order to improve the legal regulation of the budgetary process, as well as the law enforcement practice of using big data arising in the process of digitalization of the control and supervisory activities of external financial control bodies. We focus on the fact that big data analysis methods (for ex-ample, spatial analysis, social network analysis, machine learning, etc.) can be used to implement state financial control over the activities of nonprofit organizations. We find that improved methods of collecting and analyzing data helps not only to respond flexibly to sudden changes and make faster and more accurate decisions, but also to use large databases, which, in turn, allows us to move from monitoring the legality of spending to analyzing the effectiveness of use financial resources of the state. Based on the given ex-amples, we conclude that automation contributes to improving the methods of state financial control.

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Wong, Janis. "The ‘Personal’ in Personal Data: Who is Responsible for Our Data and How Do We Get it Back?" Legal Information Management 20, no. 2 (June 2020): 103–5. http://dx.doi.org/10.1017/s1472669620000249.

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In our data-driven society, every piece of technology that connects us to the internet collects our personal data (any information relating to an identified or identifiable natural person), building elaborate profiles on what we are doing, where we are, and even who we are. As data subjects (those about whom personal data are collected), we can no longer hide from data controllers (those who collect and determine what these data are used for). With every data breach and data sharing revelation from Cambridge Analytica to Google’s Project Nightingale, our personal data is becoming less personal, where data attached to our identity are no longer in our control and becomes harder for us to identify who is responsible.

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Kim, Haidong, Matthew J. Zabik, and Stanley R. Crouch. "Modification of IBM-PC Data Acquisition and Control Adaptor for Versatile Data Acquisition and Control System." Applied Spectroscopy 43, no. 4 (May 1989): 608–11. http://dx.doi.org/10.1366/0003702894202409.

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An inexpensive but versatile data acquisition system has been developed with the use of a modified IBM-DACA (IBM Personal Computer Data Acquisition and Control Adaptor). With the modification described, the IBM-DACA can be operated in more various modes of operation. Data can be read either by software trigger or by hardware trigger mode. Also, all the three channels of the on-board timer/counter can be fully utilized for complex or accurate timing and experiment control. This allows gated data acquisition with user-specified timing and a maximum A/D throughput to memory of up to 18520 samples/s in a single channel. With its menu-driven software, this system can be used in various scientific data acquisition environments with minimal expense.

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Giurgiu, Andra, and Gérard Lommel. "A New Approach to EU Data Protection – More Control over Personal Data and Increased Responsibility." Kritische Vierteljahresschrift für Gesetzgebung und Rechtswissenschaft 97, no. 1 (2014): 10–27. http://dx.doi.org/10.5771/2193-7869-2014-1-10.

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Graef, Inge, Martin Husovec, and Nadezhda Purtova. "Data Portability and Data Control: Lessons for an Emerging Concept in EU Law." German Law Journal 19, no. 6 (November 2018): 1359–98. http://dx.doi.org/10.1017/s2071832200023075.

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AbstractThe right to data portability (RtDP) introduced by Article 20 of the General Data Protection Regulation (GDPR) forms a regulatory innovation within EU law. The RtDP provides data subjects with the possibility to transfer personal data among data controllers, but has an impact beyond data protection. In particular, the RtDP facilitates the reuse of personal data that private companies hold by establishing a general-purpose control mechanism of horizontal application. Article 20 of the GDPR is agnostic about the type of use that follows from the ported data and its further diffusion. We argue that the RtDP does not fit well with the fundamental rights nature of data protection law, and should instead be seen as a new regulatory tool in EU law that aims to stimulate competition and innovation in data-driven markets.What remains unclear is the extent to which the RtDP will be limited in its aspirations where intellectual property rights of current data holders—such as copyright, trade secrets andsui generisdatabase rights—cause the regimes to clash. In such cases, a reconciliation of the interests might particularly confine the follow-on use of ported data again to specific set of socially justifiable purposes, possibly with schemes of fair remuneration. Despite these uncertainties, the RtDP is already being replicated in other fields, namely consumer protection law and the regulation of non-personal data. Competition law can also facilitate portability of data, but only for purpose-specific goals with the aim of addressing anticompetitive behavior.We conclude that to the extent that other regimes will try to replicate the RtDP, they should closely consider the nature of the resulting control and its breadth and impact on incentives to innovate. In any case, the creation of data portability regimes should not become an end in itself. With an increasing number of instruments, orchestrating the consistency of legal regimes within the Digital Single Market and their mutual interplay should become an equally important concern.

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Joy, David C. "Computers for data, control and simulation." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 920–21. http://dx.doi.org/10.1017/s0424820100106661.

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Personal computers (PCs) are a powerful resource in the EM Laboratory, both as a means of automating the monitoring and control of microscopes, and as a tool for quantifying the interpretation of data. Not only is a PC more versatile than a piece of dedicated data logging equipment, but it is also substantially cheaper. In this tutorial the practical principles of using a PC for these types of activities will be discussed.The PC can form the basis of a system to measure, display, record and store the many parameters which characterize the operational conditions of the EM. In this mode it is operating as a data logger. The necessary first step is to find a suitable source from which to measure each of the items of interest. It is usually possible to do this without having to make permanent corrections or modifications to the EM.

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KELLY, JUDITH P., LYNN ROSENBERG, DAVID W. KAUFMAN, and SAMUEL SHAPIRO. "RELIABILITY OF PERSONAL INTERVIEW DATA IN A HOSPITAL-BASED CASE-CONTROL STUDY." American Journal of Epidemiology 131, no. 1 (January 1990): 79–90. http://dx.doi.org/10.1093/oxfordjournals.aje.a115488.

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Mitropoulos, Dimitris, Thodoris Sotiropoulos, Nikos Koutsovasilis, and Diomidis Spinellis. "PDGuard: an architecture for the control and secure processing of personal data." International Journal of Information Security 19, no. 4 (September 24, 2019): 479–98. http://dx.doi.org/10.1007/s10207-019-00468-5.

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Wang, Jianfeng, Yifan Wang, and Yunling Wang. "Efficient self-adaptive access control for personal medical data in emergency setting." International Journal of Computational Science and Engineering 23, no. 4 (2020): 341. http://dx.doi.org/10.1504/ijcse.2020.10035557.

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Wang, Yifan, Yunling Wang, and Jianfeng Wang. "Efficient self-adaptive access control for personal medical data in emergency setting." International Journal of Computational Science and Engineering 23, no. 4 (2020): 341. http://dx.doi.org/10.1504/ijcse.2020.113180.

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Devedžić, Vladan, and Dušan Velašević. "Fast reasoning with external data on personal computers." Engineering Applications of Artificial Intelligence 5, no. 2 (March 1992): 87–99. http://dx.doi.org/10.1016/0952-1976(92)90049-p.

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R. Bulli Babu, Dr, P. Jagadeesh, and S. Sonia. "Cloud computing based personal health records by using data encryption." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 367. http://dx.doi.org/10.14419/ijet.v7i2.7.10721.

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Lupton, Deborah. "Feeling your data: Touch and making sense of personal digital data." New Media & Society 19, no. 10 (July 12, 2017): 1599–614. http://dx.doi.org/10.1177/1461444817717515.

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People’s encounters and entanglements with the personal digital data that they generate is a new and compelling area of research interest in this age of the ascendancy of digital data. Masses of personal information are constantly generated via people’s use of digital technologies and used for a variety of purposes by a range of actors. People are faced with the conundrum of how to interpret, control and make sense of their lively data. In this article, I explore the topic of how personal digital data and their circulations can be made more perceptible and therefore interpretable to people with the use of three-dimensional materialisations. These materialisations invite users to ‘feel your data’. As I show, ‘feeling your data’ has two meanings: the sensations of touching these three-dimensional objects and the visceral responses that are generated from these and other sensory encounters with data.

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JIA, Cangcang, and Han LIU. "Research on the Personal Privacy in the Health Big Data." E3S Web of Conferences 214 (2020): 03022. http://dx.doi.org/10.1051/e3sconf/202021403022.

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Data mining, data prediction and all-round digital monitoring of health big data make the dilemma of personal privacy control prominent. The weakening of the control of personal privacy by big data technology, the people’s data belief, the diversity of interests and the conflict of interests are the main causes of the problem of personal privacy in the context of the application of health big data. Therefore, in the application of health big data, the solution to the problem of personal privacy in the context of health big data application is to enhance the value transparency of big data technology, return and reshape humanism, and explore common values to reduce conflicts of interest.

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Batet, Montserrat, and David Sánchez. "Semantic Disclosure Control: semantics meets data privacy." Online Information Review 42, no. 3 (June 11, 2018): 290–303. http://dx.doi.org/10.1108/oir-03-2017-0090.

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Purpose To overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data. Design/methodology/approach The need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data. Findings So far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data. Practical implications SeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research. Social implications Individuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection. Originality/value On the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure.

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Lehtiniemi, Tuukka. "Personal Data Spaces: An Intervention in Surveillance Capitalism?" Surveillance & Society 15, no. 5 (December 5, 2017): 626–39. http://dx.doi.org/10.24908/ss.v15i5.6424.

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Personal data spaces, or PDSs, are emerging intermediary services that allow users control over the sharing and use of their data. In this article, the surveillance capitalism model, which describes how businesses employ datafication to create value in the digital economy, is used to contextualize PDSs. Focusing on three PDS services, I analyze the social imaginaries they represent, paying attention to the increased agency over data they offer users. This proposed agency reflects the efforts of PDSs to intervene in, but not counter, surveillance capitalism. While their goal is to intensify datafication by increasing the quality and specificity of data that businesses can employ, their interventions also change the structure of data flows, allowing users to more directly benefit from datafication. PDSs envision their users as data-supplying and benefit-demanding market participants, active subjects in value creation instead of passive objects of data extraction. PDSs view themselves as platform providers that facilitate data exchanges and rely on market mechanisms to ensure beneficial services are developed for users to choose from.

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Weydert, Valentine, Pierre Desmet, and Caroline Lancelot-Miltgen. "Convincing consumers to share personal data: double-edged effect of offering money." Journal of Consumer Marketing 37, no. 1 (August 26, 2019): 1–9. http://dx.doi.org/10.1108/jcm-06-2018-2724.

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Purpose The purpose of this paper is to demonstrate how offering control on data usage and offering money can increase willingness to share private information with a data broker. Design/methodology/approach Personal data are collected for internet users with a Web questionnaire. In an experimental framework, compensations control money are manipulated and consumers’ data sharing is explained by sensitivity and regulatory focus. Findings Offering control increases willingness to disclose personal data, even sensitive one, but the effect is not moderated by regulatory focus. Offering monetary compensation has a negative, but small, effect on willingness to share personal data, and the effect is moderated by regulatory focus. Originality/value Offering a large amount of money is a double-edged offer, as it creates a signal that increases potential negative effect of disclosing personal data to unknown third party.

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Ramakrishna, Laxmikant, Abdulfattah Mohamed Ali, and Hani Baniodeh. "Interfacing PMDC Motor to Data Port of Personal Computer." Conference Papers in Engineering 2013 (June 11, 2013): 1–6. http://dx.doi.org/10.1155/2013/218127.

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Procedures and techniques of hardware interfacing to personal computer system through parallel data port to control permanent magnet DC (PMDC) motor and create LabVIEW integrated-development-environments (IDEs) based Virtual Instrument (VI) software are discussed. To test the designed VI software diagram, authors constructed interface hardware without taking support of any commercially available DAQ boards. Hardware resource utilization and performance optimization by creating VI are discussed. Testing the design (Hardware and VI) by varying the set point speed of the motor is concluded. It is observed that the motor speed gradually approaches and locks to the desired or set speed.

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Hu, Kai, Lang Tian, Chenghang Weng, Liguo Weng, Qiang Zang, Min Xia, and Guodong Qin. "Data-Driven Control Algorithm for Snake Manipulator." Applied Sciences 11, no. 17 (September 2, 2021): 8146. http://dx.doi.org/10.3390/app11178146.

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In some environments where manual work cannot be carried out, snake manipulators are instead used to improve the level of automatic work and ensure personal safety. However, the structure of the snake manipulator is diverse, which renders it difficult to establish an environmental model of the control system. It is difficult to obtain an ideal control effect by using the traditional manipulator control method. In view of this, this paper proposes a data-driven snake manipulator control algorithm. After collecting data, the algorithm uses the strong learning and decision-making ability of the deep deterministic strategy gradient to learn these system data. A data-driven controller based on the deep deterministic policy gradient was trained in order to solve the manipulator system control problem when the control system environment model is uncertain or even unknown. The data of simulation experiments show that the control algorithm has good stability and accuracy in the case of model uncertainty.

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Yoon, KyungBae. "Study on Decision-making and Control of Personal Data Posted on the Internet." Journal of the Institute of Webcasting, Internet and Telecommunication 14, no. 4 (August 31, 2014): 227–32. http://dx.doi.org/10.7236/jiibc.2014.14.4.227.

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Lynskey, Orla. "Control over Personal Data in a Digital Age:Google SpainvAEPD and Mario Costeja Gonzalez." Modern Law Review 78, no. 3 (May 2015): 522–34. http://dx.doi.org/10.1111/1468-2230.12126.

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Brandtzaeg, Petter Bae, Antoine Pultier, and Gro Mette Moen. "Losing Control to Data-Hungry Apps: A Mixed-Methods Approach to Mobile App Privacy." Social Science Computer Review 37, no. 4 (May 31, 2018): 466–88. http://dx.doi.org/10.1177/0894439318777706.

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Personal data from mobile apps are increasingly impacting users’ lives and privacy perceptions. However, there is a scarcity of research addressing the combination of (1) individual perceptions of mobile app privacy, (2) actual dataflows in apps, and (3) how such perceptions and dataflows relate to actual privacy policies and terms of use in mobile apps. To address these limitations, we conducted an innovative mixed-methods study including a representative user survey in Norway, an analysis of personal dataflows in apps, and content analysis of privacy policies of 21 popular, free Android mobile apps. Our findings show that more than half the respondents in the user survey repeatedly had refrained from downloading or using apps to avoid sharing personal data. Our analysis of dataflows applied a novel methodology measuring activity in the apps over time (48 hr). The investigation showed that 19 of the 21 apps investigated transmitted personal data to a total of approximately 600 different primary and third-party domains. From an European perspective, it is particularly noteworthy that most of these domains were associated with tech companies in the United States, where privacy laws are less strict than companies operating from Europe. The investigation further revealed that some apps by default track and share user data continuously, even when the app is not in use. For some of these, the terms of use provided with the apps did not inform the users about the actual tracking practice. A comparison of terms of use as provided in the studied apps with actual person dataflows as identified in the analysis disclosed that three of the apps shared data in violation with their provided terms of use. A possible solution for the mobile app industry, to strengthen user trust, is privacy by design through opt-in data sharing with the service and third parties and more granular information on personal data sharing practices. Also, based on the findings from this study, we suggest specific visualizations to enhance transparency of personal dataflows in mobile apps. A methodological contribution is that a mixed-methods approach strengthens our understanding of the complexity of privacy issues in mobile apps.

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Shebanova, Nadezda Aleksandrovna. "Protection of personal data: the experience of international regional cooperation." Международное право и международные организации / International Law and International Organizations, no. 2 (February 2020): 69–87. http://dx.doi.org/10.7256/2454-0633.2020.2.32597.

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The subject of this research is the international regulation of ensuring confidentiality of personal data. The development of “big data” technologies made it evident that for solution of the problem of ensuring rights of an individual as a subject of personal data it is essential to coordinate efforts of interested countries pertaining to the development of effective measures of legal protection. The practice demonstrated that protection of personal data requires a universal approach, which is currently possible within the framework of regional integration that ensure harmonization of legislation through formulation of criteria suitable for the participating countries. However, their own rules in protection of personal data also have the countries of Latin America being a part of international organization the “Ibero-American Data Protection Network”, as well as the countries of Asia-Pacific Economic Cooperation. This article analyzes the international documents developed by the aforementioned organizations from legal and comparative-legal perspective. Assessment is given to the achieved results and development prospect of legal regulation pertaining to collection and processing of personal data. It is concluded that the European organizations are undisputable leaders in establishment of strict control in the sphere of personal data protection, and the documents developed by them are viewed as reference. The concept of “personal data” itself also experiences transformations, acquiring a more extensive interpretation. The rules for collection and processing of personal data that are developed by the participants independently, if coordinated with the legislatively established requirements, obtain substantial significance. Strengthening of control over intergovernmental exchange of personal data is also not excluded.

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Albrecht, Kat, and Brian Citro. "Data Control and Surveillance in the Global TB Response: A Human Rights Analysis." Law, Technology and Humans 2, no. 1 (April 14, 2020): 107–23. http://dx.doi.org/10.5204/lthj.v2i1.1487.

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The global response to the tuberculosis (TB) epidemic is generating copious amounts of personal health data. The emerging emphasis on the use of active case finding and digital adherence technologies in the TB response will increase the amount and expand the kind of data produced and used by public and private health officials. The production of personal data in high TB burden countries, in particular, must be considered in light of their colonial histories. In doing so, we argue that interventions to eliminate TB at global and national levels are ushering in a new era of data colonisation and surveillance in the name of public health. This, in turn, raises critical concerns for the human rights of people affected by TB, many of whom belong to vulnerable or marginalised groups. We examine the normative and legal content for a set of international human rights critical to the TB response, highlighting how each right implicates the production and use of personal health data. We also demonstrate that these rights are, by and large, enshrined in the constitutions of each high TB burden country. Finally, we use these rights to analyse active case finding and digital adherence technologies to pinpoint their unique data risks and the threats they pose to the human rights of people affected by TB.

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Lin', Do. "Legal regulation of personal data protection and its control by the state in China." Политика и Общество, no. 2 (February 2020): 1–9. http://dx.doi.org/10.7256/2454-0684.2020.2.33153.

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This article examines the Chinese normative acts that regulate personal data protection. The author reviews the questions of restrictions pertaining to personal life due to introduction of social score system. Analysis is conducted on the “system of social rating” (“social credit”) formed on the basis of government services. The article presents the examples of civil right restrictions due to low rating in the sphere of employment, public housing, reception of subsidies, basic social benefits, and loans at low interest rates. The practice of “social condemnation”, when the short clips are shown before the main film in a movie theatre naming local people who have failed to pay off debt. The conclusion is made that the social score system controls activity of a person in all spheres of social life – from business to family relations, from credit default to violation of traffic rules. The author notes that biggest unfairness of this system pertains to the citizens who buy videogames, spent long time in social network, spread fake news, which leads to restriction of high-speed Internet. It is also underlines that there is virtually no legal framework for implementation of such system or legal acts that regulate the score system, and the corresponding “guiding recommendations” of the State Council of the People’s Republic of China contain pretty vague formulations.

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Laperrière, René, Jean-Pierre Lemasson, Pierrôt Péladeau, Robert D. Bureau, and Jean Martin. "The Legal Control of Personal Data in the Private Sectors of Quebec and Canada." Canadian journal of law and society 3 (1988): 247–56. http://dx.doi.org/10.1017/s082932010000140x.

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From 1984 to 1986, the Computer Science and Law Research Group at The University of Quebec at Montreal conducted a socio-legal survey for the Government of Quebec on personal data banks in the private sector of Quebec's economy. The study flowed from a concern for the defence and promotion of individual rights and freedoms, which appeared to be threatened by the growth of relatively unregulated data banks. By furnishing an often confidential computerized double, sophisticated data bases can often give a better picture of individuals than they themselves could provide. Ultimately, decisions affecting individuals and social choices could be taken without any control over their conformity to rules of natural justice and democracy. With these concerns in mind, we studied both the current state of development of such data banks and the state of the law regulating them.

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Drinkwater, Donald E., Frantisek Turecek, and Fred W. McLafferty. "A personal computer-based data control interface for neutralization-reionization and tandem mass spectrometry." Organic Mass Spectrometry 26, no. 6 (June 1991): 559–62. http://dx.doi.org/10.1002/oms.1210260606.

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Bottis, Maria, and George Bouchagiar. "<i>Personal Data v. Big Data</i> in the EU: Control Lost, Discrimination Found." Open Journal of Philosophy 08, no. 03 (2018): 192–205. http://dx.doi.org/10.4236/ojpp.2018.83014.

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Maier, Claudia, Christian Pfeiffer, Stefanie Hatzl, and Eva Fleiß. "Influencing Factors on Consumers’ Willingness to Share Energy Data on Online Energy Platforms." Business and Management Studies 7, no. 2 (April 24, 2021): 13. http://dx.doi.org/10.11114/bms.v7i2.5161.

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Climate change requires an adaptation of the energy system towards an efficient use of renewable energies. For efficient control and optimization of the energy system, energy consumption and production data at household level play an essential role. Sharing platforms can enable the bundling and controlling of energy data from individual households. However, there is often a lack of acceptance among potential users to share their own data on such platforms. Therefore, this paper investigates the willingness of consumers to share their personal energy data. In particular, several factors that influence this willingness are examined. Decisive for the willingness are incentives for consumers in return for sharing their energy data. These can be offered in personal added value or collective added value. This paper shows that the factors perceived behavioral control, personal attitude and subjective norm have an influence on the willingness of private users to share energy data if a personal benefit or a collective benefit is provided. The age of users and their privacy concerns affect the willingness to share only in case personal value is added. These findings are valuable for the development and operation of online energy platforms.

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Abdullayeva, Fargana Dzhabar kyzy. "On One Method of Constructing Relations between Personal Data in Social Networks." Journal of Automation and Information Sciences 41, no. 1 (2009): 69–74. http://dx.doi.org/10.1615/jautomatinfscien.v41.i1.70.

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Journal articles on the topic 'Personal data control'

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