Functional or end user requirements are the tasks that the system - Protection and control of consolidated data under development is expected to perform. However, nonfunctional - Data retrieval requirements are the qualities that the system is - Equitable treatment of users to adhere to. Functional requirements are not as difficult - Data retention and disposal to tackle, as it is easier to test their implementation in the - User monitoring and protection against unauthorized system under development. Security and privacy requirements monitoring are considered nonfunctional requirements, although in many instances they do have functionality. To identify Several laws and regulations provide a set of guidelines privacy risks early in the design process, privacy requirements that can be used to assess privacy risks. For example, engineering is used (Chiasera et al. 2008). However, the Health Insurance Portability and Accountability Act unlike security requirements engineering, little attention is (HIPAA) addresses privacy concerns of health information paid to privacy requirements engineering, thus it is less mature systems by enforcing data exchange standards.

Information is shared more extensively when a user can confidently classify all his information according to its desired degree of disclosure prior to transmission. While high quality classification is relatively straightforward for structured data (e.g., credit card numbers, cookies, "confidential" reports), most consumer and business information is unstructured (e.g., Facebook posts, corporate email). All current technological approaches to classifying unstructured information seek to identify only that information having the desired characteristics (i.e., to maximize the percentage of filtered content that requires privacy protection). Such focus on boosting classifier Precision (P) causes technology solutions to miss sensitive information [i.e., Recall (R) is compromised for the sake of P improvement]. Such privacy protection will fall short of user expectations no matter how "intelligent" the technology may be in extending beyond keywords to user meaning. Systems must simultaneously optimize both P and R in order to protect privacy sufficiently to encourage the free flow of personal and corporate information. This requires a socio-technical methodology wherein the user is intimately involved in iterative privacy improvement. The approach is a general one in which the classifier can be modified as necessary at any time when sampling measures of P and R deem it appropriate. Matching the ever-evolving user privacy model to the technology solution (e.g., active machine learning) affords a technique for building and maintaining user trust.

There is a growing awareness and interest in the issues of accountability and transparency in the pursuit of digital privacy. In previous work, we asserted that systems needed to be “policy aware” and able to compute the likely compliance of any digital transaction with the associated privacy policies (law, rule, or contract). This paper focuses on one critical step in respecting privacy in a digital environment, that of understanding the context associated with each digital transaction. For any individual transaction, the pivotal fact may be context information about the data, the party seeking to use it, the specific action to be taken, or the associated rules. We believe that the granularity of semantic web representation is well suited to this challenge and we support this position in the paper.

This paper addresses the use of unmanned aircraft systems to provide embedded reasoning for atmospheric science. In particular, a specific form of heterogeneous unmanned aircraft system (UAS) is introduced. This UAS is comprised of two classes of aircraft with significantly different, though complementary, attributes: miniature daughterships that provide improved flexibility and spatio-temporal diversity of sensed data and larger motherships that carry and deploy the daughterships while facilitating coordination through increased mobility, computation, and communication. Current efforts designing unmanned aircraft for in situ sensing are described as well as future architectures for embedded reasoning by autonomous systems within complex atmospheric phenomena.

The correct recognition of behaviours based on sensor observations in a smart home is a challenging problem; the sensor observations themselves can be noisy, and the pattern activity seen for a behaviour is rarely identical for different occurrences of the behaviour. For this reason, probabilistic methods such as Hidden Markov Models are preferred over symbolic reasoning approaches. However, these models do not deal well with interleaved behaviours, nor do they allow small variations in behaviour to be detected as abnormal, although this might be useful for the smart home, since changes in ingrained habit could be early signs of illness. We propose methods for using Allen's temporal relations in order to solve these problems, and demonstrate how they can be used to recognise the interleaving of different behaviours, as well as to reason about behaviours that are frequently seen together, and therefore form a behavioural pattern or habit. In this way we have been able to extend our behaviour recognition system to recognise unusual presentations of behaviours.

This paper presents a brief summary of a number of different approaches to the semantic representation and automated interpretation of engineering data. In this context, engineering data is represented as Computer-Aided Design (CAD) files, 3D models or assemblies. Representing and reasoning about these objects is a highly interdisciplinary problem, requiring techniques that can handle the complex interactions and data types that occur in the engineering domain. This paper presents several examples, taken from different problem areas that have occupied engineering and computer science researchers over the past 15 years. Many of the issues raised by these problems remain open, and the experience of past efforts can serve to identify fertile opportunities for investigation today.

Data mining and machine learning technologies for business applications have evolved over the past two decades, and are regularly applied in contemporary organizations to everything from manufacturing to online advertising in fields ranging from health care to motor racing. Unfortunately, data mining techniques are not applied as often to problems in the developing world. Despite the fact that some industries, such as banks, airlines, courts, and telecommunications firms, necessitate data storage as part of their business process. We argue that data mining could be used to reduce infrastructure inefficiencies, which is one of the largest problems faced by Africa. We demonstrate that we can potentially reduce the infrastructure inefficiency of the Ethiopian telecommunications industry by ranking customers according to their likelihood of nonpayment using a data mining approach.

We select Google enable people to share structured data on the Web. Design sponsored link format as a basis for opinion sharing. To of web portals leverages the fact that value and usefulness encourage both business owner / advertiser and user to of data increases, when the degree of interlinks with other express their opinion in this form, we need a hybrid of data rises. It is especially true for opinion data, where trust information extraction and summarization techniques to to an aggregated opinion can be developed by a extract expressions suitable to form advertisement line demonstration of a highly interlinked sources of data of from a business web page.

The privacy dangers of data proliferation on the Web are well-known. Information on the Web has facilitated the deanonymization of anonymous bloggers, the de-sanitization of government records and the identification of individuals based on search engine queries. What has received less attention is Web-mining in support of privacy. In this position paper we argue that the very ability ofWeb data to breach privacy demonstrates its value as a laboratory for the detection of privacy breaches before they happen. In addition, we argue that privacy-invasive services may become privacy-respecting by mining publicly available Web data, with little decrease in performance and efficiency.

Most social networks have implemented extensive and complex controls in order to battle the host of privacy concerns that initially plagued their online communities. These controls have taken the form of a-priori access control, which allow users to construct barriers preventing unwanted users from viewing their personal information. However, in cases in which the access restriction mechanisms are bypassed or when the access restrictions are met but the data is later misused, this system leaves users unprotected. Our framework, Respect My Privacy, proposes an alternative approach to the protection of privacy. Our strategy is similar to how legal and social rules work in our societies where the vast majority of these rules are not enforced perfectly or automatically, yet most of us follow the majority of the rules because social systems built up over thousands of years encourage us to do so and often make compliance easier than violation. Our project aims to support similar functionality in social networks. Instead of focusing on enforcing privacy policies through restricted access, we focus on helping users conform to existing policies by making them aware of the usage restrictions associated with the data. The framework has two main functions - generating privacy or usage control policies for social networks, and visualizing these policies while exploring social networks. We have implemented this functionality across three platforms: Facebook, OpenSocial and Tabulator, a Semantic Web browser. These applications enable users to specify privacy preferences for their data and then display this privacy-annotated data prominently enabling other users to easily recognize and conform to these preferences.