April 17, 2017

Providing for privacy in a network infrastructure protection context

Machine Learning and Big Data Analysis are seen as the silver bullet to detect and counteract attacks on critical communication infrastructure. Every message is analysed and is to some degree under suspicion. The principle of innocent until proven guilty does not seem to apply to modern communication usage. On the other hand, criminals would gain easily upper hand in communication networks that are not protected and on the outlook for attacks. This poses quite a problem for the technical implementation and handling of network communication traffic. How can a communication network provider protect user data against malicious activities without screening and data analysis and loss of the human right of privacy? This article provides a classification system for data usage, privacy sensitivity and risk through which we will illustrate on a concrete example how to provide user privacy, while still enabling protection.

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Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...