The collection of personal information by organizations has become increasingly essential for social interactions. Nevertheless, according to the General Data Protection Regulation, the organizations have to protect collected data. Access Control Mechanisms (ACMs) are traditionally used to secure information systems against unauthorized access. The increased availability of personal sensor data motivates new services to offer insights about individuals. Consequently, data mining algorithms (DMAs) have been proposed to infer personal insights from collected sensor data. Although they can be used for genuine purposes, attackers can combine those outcomes with other type of data to further breach individuals’ privacy. Thus, bypassing ACMs thanks to such insights is a concrete problem. In this thesis, we address this problem by analyzing users' queries issued to a sensor database, and by identifying when they obtain sufficient information to infer insights thanks to DMAs. We refer to such a kind of inference as an Inference Attack Involving Sensor Data (IAISD). When attackers query the sensor database, the important information is not so much the exact value of the obtained data points, but rather if the relevant information are obtained according to the conditions of disclosure of DMAs. To fulfill this objective, this thesis consists in three contributions: Raw sensor data based Inference ChannEl Model (RICE-M) models the query history of a querying user which contains information obtained from queries, as well as the conditions of disclosure associated to an insight. RICE-M enables first the modeling of queries issued to a sensor database as a set of metadata units. This set constitutes the query metadata. Second, RICE-M models both the constraints that a user’s knowledge must satisfy to apply an DMA. Those descriptions correspond to the inference channels attackers leverage to perform IAISDs. The second contribution is RICE-M based inference detection System (RICE-Sy). RICE-Sy maintains a history log which keeps track of the queries metadata extracted from the queries a user issues to a sensor database. Upon receiving a new query, the related metadata is extracted and processed by the RICE-Sy. To correctly consider the current user’s knowledge, RICE-Sy retrieves from the history log the metadata units. To efficiently filter units from the history log, we endow RICE-Sy with two conceptual optimizations. The last contribution is a query metadata sequence generator which objective is to evaluate the performance of RICE-Sy. To produce realistic sequences, we identify querying behaviors by analyzing inference attack strategies and the nature of sensor databases. Thanks to the generator outcome, we evaluate RICE-Sy in terms of detection time per query and size of the history log. The results obtained validate the efficiency of our optimization, and demonstrate the feasibility of detecting IAISDs at query-time using RICE-Sy.