of generalizations and suppressions on the potentially identifying portions of the data. We extend earlier works in this area along various dimensions. First, satisfying privacy constraints is considered in conjunction with the usage for the data being disseminated. This allows us to optimize the process

Privacy concerns are becoming more important as more personal data moves online. It is therefore of interest to develop versions of data-processing algorithms which can be guaranteed to preserve the privacy of individuals ’ data. A general method for creating a privacy-preserving version of a

Abstract. The paper introduces a video surveillance and event detection framework and application for semi-supervised surveillance use. The system development follows the guidelines of national strategy for surveillance systems. The aim is to generalize the interoperability, compatibility and legality in camera surveillance systems in Hungary. The system’s intended use is in automatic mode on camera feeds that are not actively watched by surveillance personnel, and should raise alarms when unusual events occur. We present the current detector filters, and the extendable modular interface. Filters include local and global unusual motion detectors, left/stolen object detector, motion detector, tampering/failure detector, etc. It has been tested in real life situation for police street surveillance.

operations are constrained to suppress the privacy risk, it is shown that the optimal detection strategies are likelihood-ratio tests. This fundamental insight allows for the opti-mization to reuse known algorithms extended to incorporate the privacy constraint. The trade-off between the detection

of privacy constraints. The proposed framework is composed of two levels of semantic segmentation and subsequent description. The first level extracts video objects whereas the second extracts parts of a scene that can identify an individual, such as faces and license plates. The rationale behind

be in effect. When these constraints are present, they not only affect the personal data that can be collected, but also the methods that can be used to process the data. The present research aims at maximizing the personalization benefits, while at the same time satisfying the currently prevailing privacy

Abstract. Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counteracted by privacy constraints. Personalized systems need to take users ’ privacy concerns into account, as well as privacy laws and industry self

Abstract- This paper provides a general treatment of pri-vacy amplification by public discussion, a concept introduced by Bennett, Brassard, and Robert for a special scenario. Privacy amplification is a process that allows two parties to distill a secret key from a common random variable about

of social relations, such as the degree of trust based on the success of past interactions, are a powerful means to support the formation of alliances. However, by applying monitoring, also privacy concerns arise. In this paper we deal with concepts and tools to support group formations. We consider

Abstract—Data collecting agents in large networks, such as the electric power system, need to share information (measurements) for estimating the system state in a distributed manner. However, privacy concerns may limit or prevent this exchange leading to a tradeoff between state estimation