String Sanitization: A Combinatorial Approach

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user’s loc

Subframe Temporal Alignment of Non-Stationary Cameras

This paper studies the problem of estimating the sub-frame temporal off-set between unsychronized, non-stationary cameras. Based on motion trajec-tory correspondences, the estimation is done in two steps. First, we propose an algorithm to robustly estimate the frame accurate offset by analyzing the trajectories and matching their characteristic time patterns. Using this result, we then show how the estimation of the fundamental matrix between two cameras can be reformulated to yield the sub-frame accurate offset from nine correspondences. We verify the robustness and performance of our approach on synthetic data as well as on real video sequences.

String sanitization: a combinatorial approach

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user’s location history). In this paper, we consider the problem of sanitizing a string by concealing the occurrences of sensitive patterns, while maintaining data utility. First, we propose a time-optimal algorithm, TFS-ALGO, to construct