Background knowledge as used in ethnomethodology refers to commonsense reasoning. Members of society, and sociologists, use background knowledge of culture and social structure as an unstated source of guidance in their reasoning. Commonsense reasoning is also referred to as mundane reasoning.
Commonsense reasoning is a term used by ethnomethodologists, derived from Alfred Schutz, referring to the practical or everyday reasoning used by members of society to create and sustain a sense of social reality as being objective, factual, predictable and external to themselves.
Background knowledge or prior knowledge is quite simply what someone already knows about a subject, and that this background knowledge will help him gain new information. Since the objectivity of the world as a practical accomplishment is the focus of ethnomethodology this kind of reasoning is a primary topic of investigation.
People bring diverse bits of background knowledge to every subsequent experience add new information to old. Background knowledge is the basic component in learning because it helps us make sense of new ideas and experiences. Background knowledge is also information that is needed to understand a situation or problem.
A background knowledge attack is one of the most popular attacks to breach an individual's privacy. Adversaries hold prior beliefs about specific people in Smart Cities, which are also referred to as background knowledge. The difficulty in defending against a background knowledge attack is that it is not easy to model the prior beliefs of adversaries.
Privacy-MaxEnt is a generic and systematic method to integrate background knowledge in privacy quantification. According to Du et al., it can deal with many different types of background knowledge such as it is rare for males to have breast cancer, a probability or inequality, and that “Frank has pneumonia,” or “either Iris or Brian has lung cancer.”
In “Modeling and integrating background knowledge in data anonymization,” Li et al., the authors presented a framework for modeling and computing background knowledge using kernel methods.
The authors chose to use kernel regression method to approximate the probability distribution function. This kernel estimation framework has three characteristics: Focus on background knowledge that is consistent with the data; Model background knowledge as probability distributions; Use a kernel regression estimator to compute background knowledge.
All these studies about background knowledge have one thing in common: they acknowledge the difficulty in modeling an attacker's exact background knowledge. In addition, none can claim that they have addressed all types of background knowledge.