Biblio

In public video surveillance, there is an inherent conflict between public safety goals and privacy needs of citizens. Generally, societies tend to decide on middleground solutions that sacrifice neither safety nor privacy goals completely. In this paper, we propose an alternative to existing approaches that rely on cloud-based video analysis. Our approach leverages the inherent geo-distribution of fog computing to preserve privacy of citizens while still supporting camera-based digital manhunts of law enforcement agencies.

Cloud systems offer a diversity of security mechanisms with potentially complex configuration options. So far, security engineering has focused on achievable security levels, but not on the costs associated with a specific security mechanism and its configuration. Through a series of experiments with a variety of cloud datastores conducted over the last years, we gained substantial knowledge on how one desired quality like security can have a significant impact on other system qualities like performance. In this paper, we report on select findings related to security-performance trade-offs for three prominent cloud datastores, focusing on data in transit encryption, and propose a simple, structured approach for making trade-off decisions based on factual evidence gained through experimentation. Our approach allows to rationally reason about security trade-offs.