Privacy is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively.
In connection-oriented communication, a data stream is the transmission of a sequence of digitally encoded signals to convey information. Typically, the transmitted symbols are grouped into a series of packets.
Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.
Information privacy is the relationship between the collection and dissemination of data, technology, the public expectation of privacy, contextual information norms, and the legal and political issues surrounding them. It is also known as data privacy or data protection.
Internet privacy involves the right or mandate of personal privacy concerning the storage, re-purposing, provision to third parties, and display of information pertaining to oneself via the Internet. Internet privacy is a subset of data privacy. Privacy concerns have been articulated from the beginnings of large-scale computer sharing and especially relate to mass surveillance enabled by the emergence of computer technologies.
Health technology is defined by the World Health Organization as the "application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives". This includes pharmaceuticals, devices, procedures, and organizational systems used in the healthcare industry, as well as computer-supported information systems. In the United States, these technologies involve standardized physical objects, as well as traditional and designed social means and methods to treat or care for patients.
A click path or clickstream is the sequence of hyperlinks one or more website visitors follows on a given site, presented in the order viewed. A visitor's click path may start within the website or at a separate third party website, often a search engine results page, and it continues as a sequence of successive webpages visited by the user. Click paths take call data and can match it to ad sources, keywords, and/or referring domains, in order to capture data.
Digital footprint or digital shadow refers to one's unique set of traceable digital activities, actions, contributions, and communications manifested on the Internet or digital devices. Digital footprints can be classified as either passive or active. The former is composed of a user's web-browsing activity and information stored as cookies. The latter is often released deliberately by a user to share information on websites or social media. While the term usually applies to a person, a digital footprint can also refer to a business, organization or corporation.
A virtual assistant (VA) is a software agent that can perform a range of tasks or services for a user based on user input such as commands or questions, including verbal ones. Such technologies often incorporate chatbot capabilities to simulate human conversation, such as via online chat, to facilitate interaction with their users. The interaction may be via text, graphical interface, or voice - as some virtual assistants are able to interpret human speech and respond via synthesized voices.
Web tracking is the practice by which operators of websites and third parties collect, store and share information about visitors’ activities on the World Wide Web. Analysis of a user's behaviour may be used to provide content that enables the operator to infer their preferences and may be of interest to various parties, such as advertisers. Web tracking can be part of visitor management.
The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.
Dataveillance is the practice of monitoring and collecting online data as well as metadata. The word is a portmanteau of data and surveillance. Dataveillance is concerned with the continuous monitoring of users' communications and actions across various platforms. For instance, dataveillance refers to the monitoring of data resulting from credit card transactions, GPS coordinates, emails, social networks, etc. Using digital media often leaves traces of data and creates a digital footprint of our activity. Unlike sousveillance, this type of surveillance is not often known and happens discreetly. Dataveillance may involve the surveillance of groups of individuals. There exist three types of dataveillance: personal dataveillance, mass dataveillance, and facilitative mechanisms.
Crowdsensing, sometimes referred to as mobile crowdsensing, is a technique where a large group of individuals having mobile devices capable of sensing and computing collectively share data and extract information to measure, map, analyze, estimate or infer (predict) any processes of common interest. In short, this means crowdsourcing of sensor data from mobile devices.
Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.
A data management platform (DMP) is a software platform used for collecting and managing data. They allow businesses to identify audience segments, which can be used to target specific users and contexts in online advertising campaigns. DMPs may use big data and artificial intelligence algorithms to process and analyze large data sets about users from various sources. Some advantages of using DMPs include data organization, increased insight on audiences and markets, and effective advertisement budgeting. On the other hand, DMPs often have to deal with privacy concerns due to the integration of third-party software with private data. This technology is continuously being developed by global entities such as Nielsen and Oracle.
Privacy settings are "the part of a social networking website, internet browser, piece of software, etc. that allows you to control who sees information about you". With the growing prevalence of social networking services, opportunities for privacy exposures also grow. Privacy settings allow a person to control what information is shared on these platforms.
Click tracking is when user click behavior or user navigational behavior is collected in order to derive insights and fingerprint users. Click behavior is commonly tracked using server logs which encompass click paths and clicked URLs. This log is often presented in a standard format including information like the hostname, date, and username. However, as technology develops, new software allows for in depth analysis of user click behavior using hypervideo tools. Given that the internet can be considered a risky environment, research strives to understand why users click certain links and not others. Research has also been conducted to explore the user experience of privacy with making user personal identification information individually anonymized and improving how data collection consent forms are written and structured.
Soft privacy technologies fall under the category of PETs, Privacy-enhancing technologies, as methods of protecting data. Soft privacy is a counterpart to another subcategory of PETs, called hard privacy. Soft privacy technology has the goal of keeping information safe, allowing services to process data while having full control of how data is being used. To accomplish this, soft privacy emphasizes the use of third-party programs to protect privacy, emphasizing auditing, certification, consent, access control, encryption, and differential privacy. Since evolving technologies like the internet, machine learning, and big data are being applied to many long-standing fields, we now need to process billions of datapoints every day in areas such as health care, autonomous cars, smart cards, social media, and more. Many of these fields rely on soft privacy technologies when they handle data.
Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.
