The pervasiveness of location-acquisition technologies has made it possible to collect the movement data of individuals or vehicles. However, it has to be carefully managed to ensure that there is no privacy breach. In this paper, we investigate the problem of publishing trajectory data under the differential privacy model. A straightforward solution is to add noise to a trajectory - this can be done either by adding noise to each coordinate of the position, to each position of the trajectory, or to the whole trajectory. However, such naive approaches result in trajectories with zigzag shapes and many crossings, making the published trajectories of little practical use. We introduce a mechanism called SDD (Sampling Distance and Direction), which is ε-differentially private. SDD samples a suitable direction and distance at each position to publish the next possible position. Numerical experiments conducted on real ship trajectories demonstrate that our proposed mechanism can deliver ship trajectories that are of good practical utility.

The International Maritime Organization (IMO) requires a majority of cargo and passenger ships to use the Automatic Identification System (AIS) for navigation safety and traffic control. Distributing live AIS data on the Internet can offer a global view based on ships' status for both operational and analytical purposes to port authorities, shipping and insurance companies, cargo owners and ship captains and other stakeholders. Yet, uncontrolled, this distribution can seriously undermine navigation safety and security and the privacy of the various stakeholders. In this paper we present ASSIST, a system prototype based on our recently proposed access control framework, to protect data streams from unauthorized access. We demonstrate the effectiveness of the system in a real scenario with real AIS data streams.

In this paper, we tackle the problem of efficient skycube computation. We introduce a novel approach significantly reducing domination tests for a given subspace and the number of subspaces searched. Technically, we identify two types of skyline points that can be directly derived without using any domination tests. Moreover, based on formal concept analysis, we introduce two closure operators that enable a concise representation of skyline cubes. We show that this concise representation is easy to compute and develop an efficient algorithm, which only needs to search a small portion of the huge search space. We show with empirical results the merits of our approach.