J. Böhler, D. Bernau, and F. Kerschbaum, “Privacy-Preserving Outlier Detection for Data Streams,” in Data and Applications Security and Privacy XXXI - 31st Annual IFIP WG 11.3 Conference, DBSec 2017, Philadelphia, PA, USA, July 19-21, 2017, Proceedings, 2017, vol. 10359, pp. 225--238.
In cyber-physical systems sensors data should be anonymized at the source. Local data perturbation with differential privacy guarantees can be used, but the resulting utility is often (too) low. In this paper we contribute an algorithm that combines local, differentially private data perturbation of sensor streams with highly accurate outlier detection. We evaluate our algorithm on synthetic data. In our experiments we obtain an accuracy of 80% with a differential privacy value of =0.1for well separated outliers.
A. Tueno, F. Kerschbaum, D. Bernau, and S. Foresti, “Selective access for supply chain management in the cloud,” in 2017 IEEE Conference on Communications and Network Security, CNS 2017, Las Vegas, NV, USA, October 9-11, 2017, 2017, pp. 476--482.
Object-level tracking along supply chains, enabled by the low-cost and wide availability of Radio Frequency Identification (RFID) technology, permits companies to collect large amounts of data (e.g., time, location, handling) about the goods they produce. Combining the data collected by the different companies along a supply chain can provide considerable advantages to all of them. However, such a sharing sometimes needs to be selective. Indeed, companies may need to keep some information about their business operations secret. In this paper, we propose a solution that enables selective sharing of data collected along a supply chain. Our solution uses the services offered by cloud providers for sharing data among companies, and relies on selective encryption for enforcing access restrictions over such data.