Roman Matzutt

Gonzalo Munilla Garrido, Joseph Near, Aitsam Muhammad et al.

An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially private analytics: Google DP, SmartNoise, diffprivlib, diffpriv, and Chorus. We compare these libraries qualitatively (capabilities, features, and maturity) and quantitatively (utility and scalability) across four analytics queries (count, sum, mean, and variance) executed on synthetic and real-world datasets. We conclude that these libraries provide similar utility (except in some notable scenarios). However, there are significant differences in the features provided, and we find that no single library excels in all areas. Based on our results, we provide guidance for practitioners to help in choosing a suitable library, guidance for library designers to enhance their software, and guidance for researchers on open challenges in differential privacy tools for non-experts.

Roman Matzutt, Benedikt Kalde, Jan Pennekamp et al.

Popular cryptocurrencies continue to face serious scalability issues due to their ever-growing blockchains. Thus, modern blockchain designs began to prune old blocks and rely on recent snapshots for their bootstrapping processes instead. Unfortunately, established systems are often considered incapable of adopting these improvements. In this work, we present CoinPrune, our block-pruning scheme with full Bitcoin compatibility, to revise this popular belief. CoinPrune bootstraps joining nodes via snapshots that are periodically created from Bitcoin's set of unspent transaction outputs (UTXO set). Our scheme establishes trust in these snapshots by relying on CoinPrune-supporting miners to mutually reaffirm a snapshot's correctness on the blockchain. This way, snapshots remain trustworthy even if adversaries attempt to tamper with them. Our scheme maintains its retrospective deployability by relying on positive feedback only, i.e., blocks containing invalid reaffirmations are not rejected, but invalid reaffirmations are outpaced by the benign ones created by an honest majority among CoinPrune-supporting miners. Already today, CoinPrune reduces the storage requirements for Bitcoin nodes by two orders of magnitude, as joining nodes need to fetch and process only 6 GiB instead of 271 GiB of data in our evaluation, reducing the synchronization time of powerful devices from currently 7 h to 51 min, with even larger potential drops for less powerful devices. CoinPrune is further aware of higher-level application data, i.e., it conserves otherwise pruned application data and allows nodes to obfuscate objectionable and potentially illegal blockchain content from their UTXO set and the snapshots they distribute.

Roman Matzutt, Benedikt Kalde, Jan Pennekamp et al.

Bitcoin was the first successful decentralized cryptocurrency and remains the most popular of its kind to this day. Despite the benefits of its blockchain, Bitcoin still faces serious scalability issues, most importantly its ever-increasing blockchain size. While alternative designs introduced schemes to periodically create snapshots and thereafter prune older blocks, already-deployed systems such as Bitcoin are often considered incapable of adopting corresponding approaches. In this work, we revise this popular belief and present CoinPrune, a snapshot-based pruning scheme that is fully compatible with Bitcoin. CoinPrune can be deployed through an opt-in velvet fork, i.e., without impeding the established Bitcoin network. By requiring miners to publicly announce and jointly reaffirm recent snapshots on the blockchain, CoinPrune establishes trust into the snapshots' correctness even in the presence of powerful adversaries. Our evaluation shows that CoinPrune reduces the storage requirements of Bitcoin already by two orders of magnitude today, with further relative savings as the blockchain grows. In our experiments, nodes only have to fetch and process 5 GiB instead of 230 GiB of data when joining the network, reducing the synchronization time on powerful devices from currently 5 h to 46 min, with even more savings for less powerful devices.

Martin Henze, René Hummen, Roman Matzutt et al.

The increasing deployment of sensor networks, ranging from home networks to industrial automation, leads to a similarly growing demand for storing and processing the collected sensor data. To satisfy this demand, the most promising approach to date is the utilization of the dynamically scalable, on-demand resources made available via the cloud computing paradigm. However, prevalent security and privacy concerns are a huge obstacle for the outsourcing of sensor data to the cloud. Hence, sensor data needs to be secured properly before it can be outsourced to the cloud. When securing the outsourcing of sensor data to the cloud, one important challenge lies in the representation of sensor data and the choice of security measures applied to it. In this paper, we present the SensorCloud protocol, which enables the representation of sensor data and actuator commands using JSON as well as the encoding of the object security mechanisms applied to a given sensor data item. Notably, we solely utilize mechanisms that have been or currently are in the process of being standardized at the IETF to aid the wide applicability of our approach.