Towards Efficient Verification of Population Protocols

Population protocols are a well established model of computation by anonymous, identical finite state agents. A protocol is well-specified if from every initial configuration, all fair executions reach a common consensus. The central verification question for population protocols is the well-specification problem: deciding if a given protocol is well-specified. Esparza et al. have recently shown that this problem is decidable, but with very high complexity: it is at least as hard as the Petri net reachability problem, which is EXPSPACE-hard, and for which only algorithms of non-primitive recursive complexity are currently known. In this paper we introduce the class WS3 of well-specified strongly-silent protocols and we prove that it is suitable for automatic verification. More precisely, we show that WS3 has the same computational power as general well-specified protocols, and captures standard protocols from the literature. Moreover, we show that the membership problem for WS3 reduces to solving boolean combinations of linear constraints over N. This allowed us to develop the first software able to automatically prove well-specification for all of the infinitely many possible inputs.Comment: 29 pages, 1 figur

Automated unique input output sequence generation for conformance testing of FSMs

This paper describes a method for automatically generating unique input output (UIO) sequences for FSM conformance testing. UIOs are used in conformance testing to verify the end state of a transition sequence. UIO sequence generation is represented as a search problem and genetic algorithms are used to search this space. Empirical evidence indicates that the proposed method yields considerably better (up to 62% better) results compared with random UIO sequence generation

Distributed Protocols at the Rescue for Trustworthy Online Voting

While online services emerge in all areas of life, the voting procedure in many democracies remains paper-based as the security of current online voting technology is highly disputed. We address the issue of trustworthy online voting protocols and recall therefore their security concepts with its trust assumptions. Inspired by the Bitcoin protocol, the prospects of distributed online voting protocols are analysed. No trusted authority is assumed to ensure ballot secrecy. Further, the integrity of the voting is enforced by all voters themselves and without a weakest link, the protocol becomes more robust. We introduce a taxonomy of notions of distribution in online voting protocols that we apply on selected online voting protocols. Accordingly, blockchain-based protocols seem to be promising for online voting due to their similarity with paper-based protocols

SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear in USENIX Security 202

How to Work with Honest but Curious Judges? (Preliminary Report)

The three-judges protocol, recently advocated by Mclver and Morgan as an example of stepwise refinement of security protocols, studies how to securely compute the majority function to reach a final verdict without revealing each individual judge's decision. We extend their protocol in two different ways for an arbitrary number of 2n+1 judges. The first generalisation is inherently centralised, in the sense that it requires a judge as a leader who collects information from others, computes the majority function, and announces the final result. A different approach can be obtained by slightly modifying the well-known dining cryptographers protocol, however it reveals the number of votes rather than the final verdict. We define a notion of conditional anonymity in order to analyse these two solutions. Both of them have been checked in the model checker MCMAS