This is an old revision of the document!
The aim of the SPIRIT project (U. of Kent (coordination), U. de La Rochelle, U. of Essex, U. of Geneva) is to investigate the Proof-of Concept of employing novel secure and privacy-ensuring techniques in services set-up in the Internet of Things (IoT) environment, aiming to increase the trust of users in IoT-based systems. The proposed system will address distinct issues related to security and privacy, hence, overcoming the lack of user confidence, which inhibits utilisation of IoT technology.
One of the key points of the project is to study the notion of Semantic firewall. A semantic firewall must be able to allow or deny the transmission of data derived from an IoT device according to the information contained within the data and the information gathered about the requester, hence ensuring that access to such data is governed by the access permissions commensurate with the requester.
This step consists in applying information extraction (IE) techniques to attach semantics to the analysed raw data coming from IoT objects, and to produce semantic descriptors (attribute-value pairs) that are needed by the semantic firewall. The analysed raw data are (scanned) images, digital sound recording, measurements (collected by sensors), etc., accompanied by first level analysis results (document layout, component tags, extracted texts, …) Each category of raw data may in turn represent different types of content. For instance, an IoT scanned document may be a national ID card, a payslip, a medical record, etc. The aim of this WP is to devise an information extraction technique that 1) recognizes the type of content represented in the raw data and 2) adapts to the content type and utilizes background knowledge (eg. an ontology of content types) to efficiently extract the information that characterize it’s content (specific attribute values such id card number, account number, product name, …) It will be based on a combination of techniques from the fields of information extraction and knowledge representation.
The ideal candidate should have a master degree in Computer Science (or equivalent); familiarity with data and knowledge modelling and semantic technologies; good programming skills; good knowledge of English (written and spoken)and and ability to work in a collaborative environment. Proficiency in French will be a plus.
Candidates should send a CV and a motivation letter to firstname.lastname@example.org. The position is open now; selection will start immediately and continue until the position is filled. The salary will be in accordance with the rules for PhD students of Swiss national science foundation, ie. approx. CHF 4000/month x 3 years.
Prof. Gilles Falquet
Centre universitaire d'informatique - Site de Battelle, Bâtiment A
7, rte de Drize, 1227 Carouge, Suisse/Switzerland
Tel: +41 22 37 90 162, email@example.com, http://cui.unige.ch/isi