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We aim to debate and eventually be able to carefully judge how realistic the following statement of a young computer scientist is: “I would like to become an ethical correctly acting offensive cybersecurity expert”. The objective of this article is not to judge what is good and what is wrong behavior nor to present an overall solution to ethical dilemmas. Instead, the goal is to become aware of the various personal moral dilemmas a security expert may face during his work life. For this, a total of 14 cybersecurity students from HS Offenburg were asked to evaluate several case studies according to different ethical frameworks. The results and particularities are discussed, considering different ethical frameworks. We emphasize, that different ethical frameworks can lead to different preferred actions and that the moral understanding of the frameworks may differ even from student to student.
In a Semi-autonomic cloud auditing architecture we weaved in privacy enhancing mechanisms [15] by applying the public key version of the Somewhat homomorphic encryption (SHE) scheme from [4]. It turns out that the performance of the SHE can be significantly improved by carefully deriving relevant crypto parameters from the concrete cloud auditing use cases for which the scheme serves as a privacy enhancing approach. We provide a generic algorithm for finding good SHE parameters with respect to a given use case scenario by analyzing and taking into consideration security, correctness and performance of the scheme. Also, to show the relevance of our proposed algorithms we apply it to two predominant cloud auditing use cases.
Covert- and side-channels as well as techniques to establish them in cloud computing are in focus of research for quite some time. However, not many concrete mitigation methods have been developed and even less have been adapted and concretely implemented by cloud providers. Thus, we recently conceptually proposed C 3 -Sched a CPU scheduling based approach to mitigate L2 cache covert-channels. Instead of flushing the cache on every context switch, we schedule trusted virtual machines to create noise which prevents potential covert-channels. Additionally, our approach aims on preserving performance by utilizing existing instead of artificial workload while reducing covert-channel related cache flushes to cases where not enough noise has been achieved. In this work we evaluate cache covert-channel mitigation and performance impact of our integration of C 3 -Sched in the XEN credit scheduler. Moreover, we compare it to naive solutions and more competitive approaches.
We propose in this work to solve privacy preserving set relations performed by a third party in an outsourced configuration. We argue that solving the disjointness relation based on Bloom filters is a new contribution in particular by having another layer of privacy on the sets cardinality. We propose to compose the set relations in a slightly different way by applying a keyed hash function. Besides discussing the correctness of the set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. We are in particular interested in how having bits overlapping in the Bloom filters impacts the privacy level of our approach. Finally, we present our results with real-world parameters in two concrete scenarios.
This work discusses several use cases of post-mortem mobile device tracking in which privacy is required e.g. due to client-confidentiality agreements and sensibility of data from government agencies as well as mobile telecommunication providers. We argue that our proposed Bloomfilter based privacy approach is a valuable technical building block for the arising General Data Protection Regulation (GDPR) requirements in this area. In short, we apply a solution based on the Bloom filters data structure that allows a 3rd party to performsome privacy saving setrelations on a mobiletelco’s access logfile or other mobile access logfile from harvesting parties without revealing any other mobile users in the proximity of a mobile base station but still allowing to track perpetrators.
We provide a privacy-friendly cloud-based smart metering storage architecture which provides few-instance storage on encrypted measurements by at the same time allowing SQL queries on them. Our approach is most flexible with respect to two axes: on the one hand it allows to apply filtering rules on encrypted data with respect to various upcoming business cases; on the other hand it provides means for a storage-efficient handling of encrypted measurements by applying server-side deduplication techniques over all incoming smart meter measurements. Although the work at hand is purely dedicated to a smart metering architecture we believe our approach to have value for a broader class of IoT cloud storage solutions. Moreover, it is an example for Privacy-by-design supporting the positive-sum paradigm.
In this work we describe the implementation details of a protocol suite for a secure and reliable over-the-air reprogramming of wireless restricted devices. Although, recently forward error correction codes aiming at a robust transmission over a noisy wireless medium have extensively been discussed and evaluated, we believe that the clear value of the contribution at hand is to share our experience when it comes to a meaningful combination and implementation of various multihop (broadcast) transmission protocols and custom-fit security building blocks: For a robust and reliable data transmission we make use of fountain codes a.k.a. rateless erasure codes and show how to combine such schemes with an underlying medium access control protocol, namely a distributed low duty cycle medium access control (DLDC-MAC). To handle the well known problem of packet pollution of forward-error-correction approaches where an attacker bogusly modifies or infiltrates some minor number of encoded packets and thus pollutes the whole data stream at the receiver side, we apply homomorphic message authentication codes (HomMAC). We discuss implementation details and the pros and cons of the two currently available HomMAC candidates for our setting. Both require as the core cryptographic primitive a symmetric block cipher for which, as we will argue later, we have opted for the PRESENT, PRIDE and PRINCE (exchangeable) ciphers in our implementation.
In the work at hand, we combine a Private Information Retrieval (PIR) protocol with Somewhat Homomorphic Encryption (SHE) and use Searchable Encryption (SE) with the objective to provide security and confidentiality features for a third party cloud security audit. During the auditing process, a third party auditor will act on behalf of a cloud service user to validate the security requirements performed by a cloud service provider. Our concrete contribution consists of developing a PIR protocol which is proceeding directly on a log database of encrypted data and allowing to retrieve a sum or a product of multiple encrypted elements. Subsequently, we concretely apply our new form of PIR protocol to a cloud audit use case where searchable encryption is employed to allow additional confidentiality requirements to the privacy of the user. Exemplarily we are considering and evaluating an audit of client accesses to a controlled resource provided by a cloud service provider.
Remote code attestation protocols are an essential building block to offer a reasonable system security for wireless embedded devices. In the work at hand we investigate in detail the trustability of a purely software-based remote code attestation based inference mechanism over the wireless when e.g. running the prominent protocol derivate SoftWare-based ATTestation for Embedded Devices (SWATT). Besides the disclosure of pitfalls of such a protocol class we also point out good parameter choices which allow at least a meaningful plausibility check with a balanced false positive and false negative ratio.
We propose secure multi-party computation techniques for the distributed computation of the average using a privacy-preserving extension of gossip algorithms. While recently there has been mainly research on the side of gossip algorithms (GA) for data aggregation itself, to the best of our knowledge, the aforementioned research line does not take into consideration the privacy of the entities involved. More concretely, it is our objective to not reveal a node's private input value to any other node in the network, while still computing the average in a fully-decentralized fashion. Not revealing in our setting means that an attacker gains only minor advantage when guessing a node's private input value. We precisely quantify an attacker's advantage when guessing - as a mean for the level of data privacy leakage of a node's contribution. Our results show that by perturbing the input values of each participating node with pseudo-random noise with appropriate statistical properties (i) only a minor and configurable leakage of private information is revealed, by at the same time (ii) providing a good average approximation at each node. Our approach can be applied to a decentralized prosumer market, in which participants act as energy consumers or producers or both, referred to as prosumers.