Privacy engineering is an emerging field of engineering which aims to provide methodologies, tools, and techniques to ensure systems provide acceptable levels of privacy. In the US, an acceptable level of privacy is defined in terms of compliance to the functional and non-functional requirements set out through a privacy policy, which is a contractual artifact displaying the data controlling entities compliance to legislation such as Fair Information Practices, health record security regulation and other privacy laws.
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. Privacy can entail either personally identifiable information (PII) or non-PII information such as a site visitor's behaviour on a website.
Anonymity describes situations where the acting person's identity is unknown. Some writers have argued that namelessness, though technically correct, does not capture what is more centrally at stake in contexts of anonymity. The important idea here is that a person be non-identifiable, unreachable, or untrackable. Anonymity is seen as a technique, or a way of realizing, a certain other values, such as privacy, or liberty.
An anonymous P2P communication system is a peer-to-peer distributed application in which the nodes, which are used to share resources, or participants are anonymous or pseudonymous. Anonymity of participants is usually achieved by special routing overlay networks that hide the physical location of each node from other participants. Interest in anonymous P2P systems has increased in recent years for many reasons, ranging from the desire to share files without revealing one's network identity and risking litigation to distrust in governments, concerns over mass surveillance and data retention, and lawsuits against bloggers.
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances.
