2014, Henze, Martin, Bereda, Sebastian, Hummen, René, Wehrle, Klaus

As sensor networks get increasingly deployed in real-world scenarios such as home and industrial automation, there is a similarly growing demand in analyzing, consolidating, and storing the data collected by these networks. The dynamic, on-demand resources offered by today’s cloud computing environments promise to satisfy this demand. However, prevalent security concerns still hinder the integration of sensor networks and cloud computing. In this paper, we show how recent progress in standardization can provide the basis for protecting data from diverse sensor devices when outsourcing data processing and storage to the cloud. To this end, we present our Sensor Cloud Security Library (SCSlib) that enables cloud service developers to transparently access cryptographically protected sensor data in the cloud. SCSlib specifically allows domain specialists who are not security experts to build secure cloud services. Our evaluation proves the feasibility and applicability of SCSlib for commodity cloud computing environments.

2017, Ziegeldorf, Jan Henrik, Pennekamp, Jan, Hellmanns, David, Schwinger, Felix, Kunze, Ike, Henze, Martin, Hiller, Jens, Matzutt, Roman, Wehrle, Klaus

Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations.

2019, Pennekamp, Jan, Henze, Martin, Hohlfeld, Oliver, Panchenko, Andriy

Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.

2019, Glebke, René, Henze, Martin, Wehrle, Klaus, Niemietz, Philipp, Trauth, Daniel, Mattfeld, Patrick, Bergs, Thomas, Bui, Tung X.

Large-scale cyber-physical systems such as manufacturing lines generate vast amounts of data to guarantee precise control of their machinery. Visions such as the Industrial Internet of Things aim at making this data available also to computation systems outside the lines to increase productivity and product quality. However, rising amounts and complexities of data and control decisions push existing infrastructure for data transmission, storage, and processing to its limits. In this paper, we exemplarily study a fine blanking line which can produce up to 6.2 Gbit/s worth of data to showcase the extreme requirements found in modern manufacturing. We consequently propose integrated data processing which keeps inherently local and small-scale tasks close to the processes while at the same time centralizing tasks relying on more complex decision procedures and remote data sources. Our approach thus allows for both maintaining control of field-level processes and leveraging the benefits of “big data” applications.