Citation: Tuovinen L, Smeaton AF (2022) Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial. PLoS ONE 17(4): e0265997.
Public discourse on the collection and exploitation of personal data largely revolves around the model where the collection and exploitation is done by organizations while the role of the individuals is to passively allow this to happen. When the individuals are considered as active agents, typically the focus is on how they can protect their data against collection by organizations they do not trust or against exploitation for purposes they do not approve. As a concrete manifestation of this way of thinking, we may look at data protection legislation such as the General Data Protection Regulation (GDPR) of the European Union [5], which distinguishes between data subjects, i.e. natural persons who are granted various rights regarding their personal data, and data controllers and processors, i.e. organizations that are required to fulfill various obligations in order to be legally permitted to process such data.
Ad Aware Se Personal Edition Free Downloads
tag_hash_105 🔥 https://urllio.com/2yjZsL 🔥
In many cases, data collected using personal wellness products can be exported from a website with relatively little effort, but this in itself does not help unless the user also has the skills required to analyze the data. Most people do not have such skills, nor can they be realistically expected to acquire them. However, if the owner of the data is able to find someone who does have the required skills and is willing to help, the two can collaborate to refine the data into knowledge that the data owner is interested in. To support such collaborations, we have developed an experimental online software platform that enables data owners and data analysts to find one another, communicate and exchange datasets and analysis results over the Internet.
To test the validity of the concept of collaborative analysis and the ability of the platform to support it, we conducted a trial in which a number of volunteers collected sleep data using wearable sensor devices and used the platform to study this data in collaboration with others who were data analysts in the trial. The trial demonstrated the fundamental viability of the ontology-based architecture. Furthermore, the results of a feedback survey completed by participants after the trial indicate that the concept and process of collaborative analysis of personal data using a platform wity the characteristics of the one used here are valid and should be studied further.
In the remainder of the paper, we first present essential background information and review related work. We then discuss the concept, process, tasks and challenges of collaborative personal data analytics. This is followed by a description of the functionality, architecture and implementation of the collaboration platform. Finally, we present our validation results and discuss them critically before concluding the paper.
Terms such as lifelogging [6] and the quantified self [7] have been used to refer to the practice of an individual using technology to collect, archive and analyze data representing various aspects of their own life. A wide range of personal sensor devices may be used to collect such data, and there is likewise a wide range of potential applications for such data. However, many of these have had a limited impact among the general populace; for example, using a wearable camera such as SenseCam [8] to automatically capture a continuous stream of images remains a niche activity.
One form of personal data collection that has become notably popular is activity and sleep tracking. According to a survey carried out in 2019, 21% of American adults regularly wear a fitness tracker or smart watch [9]. The products available for this purpose include a diverse range of wearable devices, ambient sensors (placed under the mattress or on a bedside table, for example) and mobile software applications (using sensors commonly included in smartphones, such as accelerometers and microphones). Some of these only provide an interface for viewing the data, making them unsuitable for collecting data to be analyzed using a different application, but there are many that allow a user to export the data in a portable format such as CSV, enabling the user to import the data into any of a large number of data analysis tools.
The process of collaborative analysis of personal data can be modeled as a sequence of two cycles. In the negotiation cycle, the collaborators agree on the terms of the collaboration; in the analysis cycle, they pursue the objective defined in the analysis cycle.
The most important part of the functionality of the platform is the management and sharing of datasets. There are a few special requirements associated with this, arising from the nature of this type of collaboration. The process of collaborative personal analytics driven by the data owner: the data processed and the analysis tasks carried out in a given collaboration are ultimately determined by what data the owner has collected, what the owner is willing to share with others and what kind of knowledge the owner is interested in. The data owner should therefore have control over where, when and how the data is stored and processed, insofar as this is practical.
In the profile section, the user can view and edit their personal details. These include their login information (username and password), real name (or the name they want other users to see), profile text and expertise. Areas of expertise are stored in the underlying ontology and described with keywords; when specifying their areas expertise, the user can choose from those already available in the ontology or create new ones. Any number of areas of expertise may be specified for a given user.
When a dataset is added to a collaboration, the corresponding ontology individuals are linked together by the appropriate object property. All users participating in the collaboration will then have access to metadata about the dataset and can request the owner of the dataset to grant them access to its contents. When a data request is granted by the data owner, applicable privacy constraints are first evaluated to identify any parts of the dataset to be excluded from being shared. The values of data items not excluded are then uploaded to the server, which will maintain a temporary copy of the data until the sender of the data request downloads it. Once the download is complete, the server will delete the temporary copy.
The platform is implemented as a Java-based client-server system with an underlying domain ontology providing essential knowledge representation and reasoning capabilities. To validate the platform, a trial was carried out, with 12 volunteers collecting sleep data using wearable devices and analyzing it in collaboration with an expert. The results of the trial demonstrate the viability of the collaborative personal analytics concept and the feasibility of the ontology-based architecture of the platform.
Since 2004, the President of the United States and Congress have declared the month of October to be Cybersecurity Awareness Month, a dedicated month for the public and private sectors to work together to raise awareness about the importance of cybersecurity.
Over the years it has grown into a collaborative effort between government and industry to enhance cybersecurity awareness, encourage actions by the public to reduce online risk and generate discussion on cyber threats on a national and global scale. October 2023 marked the 20th Cybersecurity Awareness Month.
As remote work remains the norm for many people, organizations continue to seek out solutions to protect corporate data regardless of where employees are located and which devices they use. In particular, monitoring for potential data exposures can be a challenging task when employees or contractors use unmanaged devices, as there is risk of downloading unauthorized data or accidentally uploading sensitive files to personal accounts.
New Data Loss Prevention (DLP) rules with Context-Aware conditions can provide the ability to control sensitive information transfers based on user and device attributes. An admin can create targeted rules that limit access based on the user's device information, such as only allow access to users with Chrome Managed Browsers. Such rules are a key ingredient for organizations that want to protect their data from unauthorized access, particularly when data is being accessed on personal devices, external networks, or restricted geo locations.
Self-awareness is the ability to monitor our inner and external world. Our thoughts and feelings arise as signals. Developing self-awareness allows us to keep from being swept away by those signals, and instead, objectively and thoughtfully respond to them. Self-aware people understand their internal experience and their impact on the experience of others.
When working on self-awareness, it is essential to do so from a self-distanced perspective, with a focus on reasons underlying emotional experience rather than what was emotionally experienced (Kross, Ayduk, & Mischel, 2005).
Developing self-awareness requires higher level cognitive processing. It requires an information-gathering perspective. This processing results in increases in adaptability and flexibility. Having increased self-awareness builds resilience (Hippe, 2004). Self-awareness also improves our ability to empathize with others (Younas, Rasheed, Sundus, & Inayat, 2020).
When compassion and empathy rise, so does the higher self. With intentions and purpose, a self-aware human can significantly impact the world around them. Self-aware people tend to show up with self-confidence, self-worth, and high success rates (Duval & Silvia, 2002).
Starting a mindfulness practice is another way to increase self-awareness. There are a variety of activities to include in a mindfulness practice. Find a few ideas to inspire you to incorporate meditation, yoga, or some other variation to improve your presence. When mindfulness is practiced, behavior becomes more intentional, and increased self-awareness develops. 0852c4b9a8
where to download guitar pro 5 for free
sony vegas pro 9 free download no virus