Roman Matzutt

Maintaining User Control While Storing and Processing Sensor Data in the Cloud

Clouds provide a platform for efficiently and flexibly aggregating, storing, and processing large amounts of data. Eventually, sensor networks will automatically collect such data. A particular challenge regarding sensor data in Clouds is the inherent sensitive nature of sensed information. For current Cloud platforms, the data owner loses control over her sensor data once it enters the Cloud. This imposes a major adoption barrier for bridging Cloud computing and sensor networks, which we address henceforth. After analyzing threats to sensor data in Clouds, the authors propose a Cloud architecture that enables end-to-end control over sensitive sensor data by the data owner. The authors introduce a well-defined entry point from the sensor network into the Cloud, which enforces end-to-end data protection, applies encryption and integrity protection, and grants data access. Additionally, the authors enforce strict isolation of services. The authors show the feasibility and scalability of their Cloud architecture using a prototype and measurements.

A Trust Point-based Security Architecture for Sensor Data in the Cloud

The SensorCloud project aims at enabling the use of elastic, on-demand resources of today’s Cloud offers for the storage and processing of sensed information about the physical world. Recent privacy concerns regarding the Cloud computing paradigm, however, constitute an adoption barrier that must be overcome to leverage the full potential of the envisioned scenario. To this end, a key goal of the SensorCloud project is to develop a security architecture that offers full access control to the data owner when outsourcing her sensed information to the Cloud. The central idea of this security architecture is the introduction of the trust point, a security-enhanced gateway at the border of the information sensing network. Based on a security analysis of the SensorCloud scenario, this chapter presents the design and implementation of the main components of our proposed security architecture. Our evaluation results confirm the feasibility of our proposed architecture with respect to the elastic, on-demand resources of today’s commodity Cloud offers.

Secure and anonymous decentralized Bitcoin mixing

The decentralized digital currency Bitcoin presents an anonymous alternative to the centralized banking system and indeed enjoys widespread and increasing adoption. Recent works, however, show how users can be reidentified and their payments linked based on Bitcoins most central element, the blockchain, a public ledger of all transactions. Thus, many regard Bitcoins central promise of financial privacy as broken.In this paper, we propose CoinParty, an efficient decentralized mixing service that allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. CoinParty, through a novel combination of decryption mixnets with threshold signatures, takes a unique place in the design space of mixing services, combining the advantages of previously proposed centralized and decentralized mixing services in one system. Our prototype implementation of CoinParty scales to large numbers of users and achieves anonymity sets by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain. CoinParty can easily be deployed by any individual group of users, i.e.,independent of any third parties, or provided as a commercial or voluntary service, e.g.,as a community service by privacy-aware organizations. We present ideal properties for mixing of digital currencies.We propose a novel efficient decentralized mixing service for Bitcoin.A novel oblivious shuffle protocol improves resilience against malicious attackers.Use of threshold cryptography increases anonymity and enables deniability.The system is usable, scalable and compatible with Bitcoin/other digital currencies.

BLOOM: BLoom filter based oblivious outsourced matchings

BackgroundWhole 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.MethodsWe propose Fhe-Bloom and Phe-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance.ResultsWe implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while Phe-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries.ConclusionsBoth approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, Fhe-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, Phe-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.

Distributed Configuration, Authorization and Management in the Cloud-Based Internet of Things

Network-based deployments within the Internet of Things increasingly rely on the cloud-controlled federation of individual networks to configure, authorize, and manage devices across network borders. While this approach allows the convenient and reliable interconnection of networks, it raises severe security and safety concerns. These concerns range from a curious cloud provider accessing confidential data to a malicious cloud provider being able to physically control safety-critical devices. To overcome these concerns, we present D-CAM, which enables secure and distributed configuration, authorization, and management across network borders in the cloud-based Internet of Things. With D-CAM, we constrain the cloud to act as highly available and scalable storage for control messages. Consequently, we achieve reliable network control across network borders and strong security guarantees. Our evaluation confirms that D-CAM adds only a modest overhead and can scale to large networks.

Practical Data Compliance for Cloud Storage

Despite their increasing proliferation and technical variety, existing cloud storage technologies by design lack support for enforcing compliance with regulatory, organizational, or contractual data handling requirements. However, with legislation responding to rising privacy concerns, this becomes a crucial technical capability for cloud storage systems. In this paper, we introduce PRADA, a practical approach to enforce data compliance in key-value based cloud storage systems. To this end, PRADA introduces a transparent data handling layer which enables clients to specify data handling requirements and provides operators with the technical means to adhere to them. The evaluation of our prototype shows that the modest overheads for supporting data handling requirements in cloud storage systems are practical for real-world deployments.

POSTER: I Don't Want That Content! On the Risks of Exploiting Bitcoin's Blockchain as a Content Store

Bitcoin has revolutionized digital currencies and its underlying blockchain has been successfully applied to other domains. To be verifiable by every participating peer, the blockchain maintains every transaction in a persistent, distributed, and tamper-proof log that every participant needs to replicate locally. While this constitutes the central innovation of blockchain technology and is thus a desired property, it can also be abused in ways that are harmful to the overall system. We show for Bitcoin that blockchains potentially provide multiple ways to store (malicious and illegal) content that, once stored, cannot be removed and is replicated by every participating user. We study the evolution of content storage in Bitcoins blockchain, classify the stored content, and highlight implications of allowing the storage of arbitrary data in globally replicated blockchains.

Thwarting Unwanted Blockchain Content Insertion

Since the introduction of Bitcoin in 2008, blockchain systems have seen an enormous increase in adoption. By providing a persistent, distributed, and append-only ledger, blockchains enable numerous applications such as distributed consensus, robustness against equivocation, and smart contracts. However, recent studies show that blockchain systems such as Bitcoin can be (mis) used to store arbitrary content. This has already been used to store arguably objectionable content on Bitcoins blockchain. Already single instances of clearly objectionable or even illegal content can put the whole system at risk by making its node operators culpable. To overcome this imminent risk, we survey and discuss the design space of countermeasures against the insertion of such objectionable content. Our analysis shows a wide spectrum of potential countermeasures, which are often combinable for increased efficiency. First, we investigate special-purpose content detectors as an ad hoc mitigation. As they turn out to be easily evadable, we also investigate content-agnostic countermeasures. We find that mandatory minimum fees as well as mitigation of transaction manipulability via identifier commitments significantly raise the bar for inserting harmful content into a blockchain.

myneData: Towards a Trusted and User-controlled Ecosystem for Sharing Personal Data

Personal user data is collected and processed at large scale by a handful of big providers of Internet services. This is detrimental to users, who often do not understand the privacy implications of this data collection, as well as to small parties interested in gaining insights from this data pool, e.g., research groups or small and middle-sized enterprises. To remedy this situation, we propose a transparent and user-controlled data market in which users can directly and consensually share their personal data with interested parties for monetary compensation. We define a simple model for such an ecosystem and identify pressing challenges arising within this model with respect to the user and data processor demands, legal obligations, and technological limits. We propose myneData as a conceptual architecture for a trusted online platform to overcome these challenges. Our work provides an initial investigation of the resulting myneData ecosystem as a foundation to subsequently realize our envisioned data market via the myneData platform.