Such a trajectory is approximated as piece-wise linear among timestamps where objects (small) velocity changes; a storage efficient strategy adopted in our previous work [9]. Some previous works investigate the problem of \(CkNN\) for moving objects with fixed velocity while others with uncertain velocity in road networks [10; 11]. Nonetheless, once an object's velocity changes, the \(CkNN\) query has to be re-executed. Such a case is quite frequent in highly dynamic environments, thus, the performance of these techniques would be significantly degraded, resulting in increased query cost (because of re-evaluation). The focus of this work is on \(k\) nearest neighbor queries, which are considered under both a spatio-temporal and a Hough transformed database. The Hough method can mitigate such issues since it is piece-wise constant [9], thus, the \(CkNN\) query is not evaluated as frequent as in the Euclidean space with non-linear trajectory. Actually, the application of \(k\)-NN queries in Hough space facilitates the maintenance and consistency of continuous queries result [12], even if the moving objects location data updates. The aforementioned issue relates to the fact that the query result needs to be only updated upon specific changes, namely, whenever moving angle direction and/or velocity vary dramatically. Nevertheless, while an object (including the query object) is moving on a linear part of its trajectory (maintenance phase) the query result will not change, meaning that the query result of all the objects will not need be revised until the objects transit to the next linear part of its trajectory, which indicates that the maintenance phase expires. Moreover, with the extensive adoption of the Cloud, research on privacy preserving has appealed to lots of researchers [13; 14]. Let us recall that the ultimate purpose of the processing is to formulate the \(k\)-anonymity set to protect users' privacy in the cloud computing environment. Especially, in the context of this work, we are challenged with the necessity to apply temporal continuous spatial \(k\)-NN queries in a distributed architecture with the ultimate goal of forming the anonymity set for a group of moving objects in a fast and efficient manner. In summary, the main contributions of this paper are as follows:* A continuous query processing algorithm is considered that efficiently answers the spatial \(k\)-NN query with the aid of an indexing method or otherwise. From the query result, in each timestamp and for all moving objects, the desired anonymity set is formulated. * The adopted method is designed with the aid of GeoSpark spatial data operations to compute the \(k\)-anonymity set which is used to measure the possibility of each object identity being unveiled as it moves from one location to another. To be more specific, vulnerability evaluation is conducted in two-dimensional space (both Euclidean and Hough space), considering different pairs of features, as it will be demonstrated in Section 4. * A comprehensive set of experiments is conducted to evaluate the time performance of the proposed method in the GeoSpark environment under different data sizes. The rest of this paper is organized as follows. In Section 2, previous related works are presented in relation to our approach. In Section 3, the following are described: (a) the problem definition, (b) the system model and (c) the proposed GeoSpark framework for the \(k\)-anonymity set computation. Section 4 presents the experiments conducted for the evaluation of the studying problem, while Section 5 evaluates relevant discussion of the studying problem results in relation to previous works. Finally, in Section 6, conclusions and future directions of this work are recorded.