Traditional resilient systems operate on fully-replicated fault-tolerant clusters, which limits their scalability and performance. One way to make the step towards resilient high-performance systems that can deal with huge workloads is by enabling independent fault-tolerant clusters to efficiently communicate and cooperate with each other, as this also enables the usage of high-performance techniques such as sharding. Recently, such inter-cluster communication was formalized as the Byzantine cluster-sending problem. Unfortunately, existing worst-case optimal protocols for cluster-sending all have linear complexity in the size of the clusters involved. 

In this paper, we propose probabilistic cluster-sending techniques as a solution for the cluster-sending problem with only an expected constant message complexity, this independent of the size of the clusters involved and this even in the presence of highly unreliable communication. Depending on the robustness of the clusters involved, our techniques require only two-to-four message round-trips (without communication failures). Furthermore, our protocols can support worst-case linear communication between clusters. Finally, we have put our techniques to the test in an in-depth experimental evaluation that further underlines the exceptional low expected costs of our techniques in comparison with other protocols. As such, our work provides a strong foundation for the further development of resilient high-performance systems.

Solution: Byzantine Cluster-Sending in Expected Constant Cost and Constant Time

To enable scalable resilient blockchain systems, several powerful general-purpose approaches toward sharding such systems have been demonstrated. Unfortunately, these approaches all come with substantial costs for ordering and execution of multi-shard transactions.

In this work, we ask whether one can achieve significant cost reductions for processing multi-shard transactions by limiting the type of workloads supported. To initiate the study of this problem, we propose Cerberus, a family of minimalistic primitives for processing single-shard and multi-shard UTXO-like transactions. The first Cerberus variant we propose is core-Cerberus (CCerberus). CCerberus uses strict UTXO-based environmental requirements to enable powerful multi-shard transaction processing with an absolute minimum amount of coordination between shards. In the environment we designed CCerberus for, CCerberus will operate perfectly with respect to all transactions proposed and approved by well-behaved clients, but does not provide any other guarantees. 

To illustrate that CCerberus-like protocols can also be of use in environments with faulty clients, we also demonstrate two generalizations of CCerberus, optimistic-Cerberus and resilient-Cerberus, that make different tradeoffs in complexity and costs when dealing with faulty behavior and attacks. Finally, we compare these three protocols and show their potential scalability and performance benefits over state-of-the-art general-purpose systems. These results underline the importance of the study of specialized approaches toward sharding in resilient systems.  

Problem: Cerberus: Minimalistic Multi-shard Byzantine-resilient Transaction Processing

Some recent services use a sequencer to simplify ordering operations on sharded data. The sequencer assigns each operation a multi-sequence number which explicitly orders the operation on each shard it accesses. Existing sequencers have two shortcomings. First, failures can result in some multi-sequence numbers never being assigned, exposing a noncontiguous multi-sequence, which requires complex scaffolding to handle. Second, existing implementations use single-machine sequencers, limiting service throughput to the ordering throughput of one machine.

We make two contributions. First, we posit that sequencers should expose our new contiguous multi-sequence abstraction. Contiguity guarantees every sequence number is assigned an operation, simplifying the abstraction. Second, we design and implement MASON, the first system to expose the contiguous multi-sequence abstraction and the first to provide a scalable multi-sequence. MASON is thus an ideal building block for consistent, scalable services. Our evaluation shows MASON unlocks scalable throughput for two strongly-consistent services built on it.

Revision: Solution: Scalable, Contiguous Sequencing for Building Consistent Services

Mobile devices are producing larger and larger data streams, such as location streams, which are consumed by location-based services to deliver personalized content to end users. Such data streams are generally uploaded and centralized to be processed by third parties, potentially exposing sensitive personal information. In this context, existing protection mechanisms, such as Location Privacy Protection Mechanisms (LPPM), have been investigated. Alas, none of them have actually been implemented, nor deployed in real-life, in mobile devices to enforce user privacy at the edge. We believe that the effective deployment of LPPM on mobile devices faces a major challenge: the storage of unbounded data streams.
This paper introduces FLAIR, a storage system based on a new piece-wise linear approximation technique that increases the storage capacity of mobile devices by relying on data modeling. Beyond the FLAIR storage layer, we also introduce Divide & Stay, a new privacy-preserving technique to execute Points Of Interest (POI) inference. Finally, we deploy both of them on Android and iOS to demonstrate that a real deployment of LPPM is now possible.

FLAIR: Storing unbounded data streams on mobile devices to unlock user privacy at the edge

Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward.

JSYS 2023 March Papers Submissions

Solution: Byzantine Cluster-Sending in Expected Constant Cost and Constant Time

Problem: Cerberus: Minimalistic Multi-shard Byzantine-resilient Transaction Processing

Revision: Solution: Scalable, Contiguous Sequencing for Building Consistent Services

FLAIR: Storing unbounded data streams on mobile devices to unlock user privacy at the edge

SoK: Evaluations in Industrial Intrusion Detection Research