We address the challenge of constructing a proof system capable of handling multiple computations that involve diverse types of tasks, such as scalable zkRollup applications. A central dilemma in this design is the trade-off between generality and efficiency: while arithmetic circuit-based SNARKs offer fast proofs but limited flexibility, zkVMs provide general-purpose programmability at the cost of considerable overhead for circuit translation. We observe that typical workloads for such applications can be naturally divided into two parts: (1) diverse, task and data-dependent application logic, and (2) computationally intensive cryptographic operations, e.g., hashes, that are common and repetitive. To optimize for both efficiency and adaptability, we propose Gryphes, a hybrid framework that composes matrix lookup, a generalization of lookup arguments, together with SNARK solutions tailored for cryptographic operations. At the heart of ame is a novel and efficient linking protocol, enabling seamless, efficient composition of matrix lookup + Plonk with general commit-and-prove SNARKs. By integrating Gryphes with Groth16 for signatures and RSA accumulators for membership proofs, we build a zkRollup prototype that achieves efficient proving, constant-size proofs, and dynamic support for thousands of transaction types. This includes our matrix lookup implementation incorporated with Plonk, as well as practical optimizations, comprehensive benchmarks, and open-sourced code. Our results demonstrate that Gryphes strikes a very good balance between functionality and efficiency, offering highly expressive and practical zkRollup systems.

Time-lock puzzles are cryptographic primitives that guarantee to the generator that the puzzle cannot be solved in less than$T$sequential computation steps. They have recently found numerous applications, e.g., in fair contract signing and seal-bid auctions. However, solvers have no a priori guarantee about the solution they will reveal, e.g., about its “usefulness” within a certain application scenario. In this work, we propose verifiable time-lock puzzles (VTLPs) that address this by having the generator publish a succinct proof that the solution satisfies certain properties (without revealing anything else about it). Hence solvers are now motivated to “commit” resources into solving the puzzle. We propose VTLPs that support proving arbitrary NP relations$\mathcal{R}$about the puzzle solution. At a technical level, to overcome the performance hurdles of the “naive” approach of simply solving the puzzle within a SNARK that also checks$\mathcal{R}$, our scheme combines the “classic” RSA time-lock puzzle of Rivest, Shamir, and Wagner, with novel building blocks for “offloading” expensive modular group exponentiations and multiplications from the SNARK circuit. We then propose a second VTLP specifically for checking RSA-based signatures and verifiable random functions (VRFs). Our second scheme does not rely on a SNARK and can have several applications, e.g., in the context of distributed randomness generation. Along the road, we propose new constant-size proofs for modular exponent relations over hidden-order groups that may be of independent interest. Finally, we experimentally evaluate the performance of our schemes and report the findings and comparisons with prior approaches.

Crowdsensing recently attracts great attention from both industry and academia. By fusing and analyzing multi- dimensional sensing data collected from crowdsensing users, it is possible to support health caring, environment mentoring, traffic mentoring and social behavior mentoring. Nonetheless, how to preserve users' data privacy during data fusing, e.g., data aggregation, has been rarely discussed for crowdsensing before. Besides, due to the dynamics of sensing environments and available resources at users, there will be missing elements from users' sensing results. In this paper we aim to achieve privacy-preserving data aggregation over incomplete data for crowdsensing. A novel scheme is developed based on linear transformation and homomorphic encryption scheme. It enables the server to obtain aggregation results over recovered sensing results without learning their individual details. Security analysis and performance evaluation are conducted showing the effectiveness and efficiency of our scheme.

The database-driven cognitive radio network (CRN) is regarded as a promising way for a better utilization of spectrum resources without introducing the interference to primary users (PUs). However, there are some critical security and privacy issues in database-driven CRNs, which have been rarely discussed before. First of all, in order to retrieve the spectrum available information (SAI) of one's vicinity, an SU's query will inevitably disclose its location information. Second, malicious SUs may query SAI for other locations so as to infer operational patterns of PUs and other SUs. In addition, they can reconstruct the entire SAI of the database and sell it for profit. Therefore, in this paper we aim to guarantee both location privacy of SUs and information security of the database during spectrum query in database-driven CRNs. We first leverage private information retrieval (PIR) techniques to allow the database to find out the SAI regarding a querying SU's location, without learning the query information, i.e., this SU's location. To prevent malicious SUs inferring SAI of other locations, SUs are required to provide location proofs indicating that they are at the places where they claim to be. Theoretical analysis is provided showing that our scheme is privacy-preserving and secure. Experiments are also conducted to evaluate the its efficiency.

Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery. In addition, energy cost of transmission underwater is much higher than reception (almost 125:1 [1]). Thus, collision and retransmission should be reduced in order to reduce energy cost and improve throughput Receiver-based protocols, like RIPT and COS-TS, can significantly reduce collision and retransmission. But they are time and energy consuming because nodes are controlled to turn into receiver mode by control packets or a timer regardless of load. In this paper, we propose an underwater practical MAC protocol, called UPMAC. The main objective of UPMAC is to adapt to the network load conditions by providing two modes (high and low load modes) and switching between them based on different offered load. Turn-around time overhead is reduced and it is less vulnerable to control packet corruption, since we reduce the use of control packets by the technique of piggyback. UPMAC provides a low data collision rate in both one-hop and multi-hop situation because we use Receiver-based approach in high load mode. Extensive simulations show that our approach can achieve significantly better performance in both general and Sea Swarm (tree) topologies.