Zk-Oracle: trusted off-chain compute and storage for decentralized applications

An authenticated data structure (ADS) is a data structure whose operations can be carried out by an untrusted prover, the results of which a verifier can efficiently check as authentic. This is done by having the prover produce a compact proof that the verifier can check along with each operation's result. ADSs thus support outsourcing data maintenance and processing tasks to untrusted servers without loss of integrity. Past work on ADSs has focused on particular data structures (or limited classes of data structures), one at a time, often with support only for particular operations.

Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users’ direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers.

On Credibility, Clarity, and Compliance

With the evolution of Web3.0 and decentralized applications (Dapps), increasing business logic is running on the blockchain. Blockchain storage, as the core infrastructure supporting the increasing volume of data, plays a crucial role. However, the performance and cost of blockchain storages are suffering seriously, which inspires us to re-examine the design of blockchain storage based on the blockchain data characteristics. We propose LETUS, a Log-structured Efficient Trusted Universal Storage for blockchain, providing cryptographic tamper evidence with excellent performance and resource efficiency. (1) LETUS breaks the traditional two-layered architecture and pushes down the Authenticated Data Structure (ADS) into the storage engine to enable fine-grained I/O optimizations. (2) LETUS proposes DMM-Tree which is a novel ADS combining the functionalities of Merkle tree and delta-encoding, significantly reducing storage consumption. (3) LETUS adopts a version-based indexing schema and manages the large volume of pages generated by ADS in a page store indexed by a B-tree variant. (4) LETUS provides a universal solution for different blockchains, such as public blockchains like Ethereum, BNB Smart Chain and AntChain as a representation of consortium blockchains. LETUS has been deployed in AntChain commercial applications, such as NFT and digital torch ignition for 2023 Asian Games. Experimental results also show that with LETUS, AntChain can achieve up to 15.8× improvement in throughput and 80.3% storage cost saving, Ethereum can achieve up to 10.1× improvement in throughput and 75.0% storage cost saving.

In an outsourced database scheme, the data owner delegates the data management tasks to a remote service provider. At a later time, the remote service is supposed to answer any query on the database. The essential requirements are ensuring the data integrity and authenticity with efficient mechanisms. Current approaches employ authenticated data structures to store security information, generated by the client and used by the server, to compute proofs that show the answers to the queries are authentic. The existing solutions have shortcomings with multi-clause queries and duplicate values in a column. We propose a hierarchical authenticated data structure for storing security information, which alleviates the mentioned problems. We provide a unified formal definition of a secure outsourced database scheme, and prove that our proposed scheme is secure according to this definition, which captures previously separate properties such as correctness, completeness, and freshness. The performance evaluation based on our prototype implementation confirms the efficiency of the proposed outsourced database scheme, showing more than 50 % decrease in proof size and proof generation time compared to previous work, and about 1–20 % communication overhead compared to the query result size.

Potentially toxic elements in soils of Campania region (Southern Italy): Combining raw and compositional data

Authenticated data storage on an untrusted platform is an important computing paradigm for cloud applications ranging from data outsourcing, to cryptocurrency and general transparency logs. These modern applications increasingly feature update-intensive workloads, whereas existing authenticated data structures (ADSs) designed with in-place updates are inefficient to handle such workloads. This work addresses the issue and presents a novel authenticated log-structured merge tree (eLSM) based key-value store built on Intel SGX. We present a system design that runs the code of eLSM store inside enclave. To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels. We design protocols to support data integrity, (range) query completeness, and freshness. Our protocol causes small proofs by including the Merkle proofs at selected levels. We implement eLSM on top of Google LevelDB and Facebook RocksDB with minimal code change and performance interference. We evaluate the performance of eLSM under the YCSB workload benchmark and show a performance advantage of up to 4.5X speedup.

There are a lot of studies about fake news detection on English social networks. However, Vietnamese fake news detection on social networks still limit. In this paper, we propose a new approach for Vietnamese Fake News Detection on Social Network Sites using a pre-train language model PhoBERT combine with Term Frequency - Inverse Document Frequency (TF-IDF) for word embedding and Convolutional Neural Network (CNN) for features extracting. Our proposed model is trained and evaluated on the dataset of Reliable Intelligence Identification on Vietnamese SNSs (ReINTEL) shared task. We process text data into two scenarios: raw data and processed data to elucidate the hypothesis of pre-processing data on social networks. In addition, we use the different extra features to improve the efficiency of model. We compare our proposed model with the baseline methods. The proposed model achieved outstanding results with 0.9538 AUC score on raw data.

In this paper, we show for the first time it is practical to privately delegate proof generation of zkSNARKs to a single server for computations of up to 2^20 R1CS constraints. We achieve this by computing zkSNARK proof generation over homomorphic ciphertexts, an approach we call blind zkSNARKs. We formalize the concept of blind proofs, analyze their cryptographic properties and show that the resulting blind zkSNARKs remain sound when compiled using BCS compilation. Our work follows the framework proposed by Garg et al. (Crypto'24) and improves the instantiation presented by Aranha et al. (Asiacrypt'24), which implements only the FRI subprotocol. By delegating proof generation, we are able to reduce client computation time from 10 minutes to mere seconds, while server computation time remains limited to 20 minutes. We also propose a practical construction for vCOED supporting constraint sizes four orders of magnitude larger than the current state-of-the-art verifiable FHE-based approaches. These results are achieved by optimizing Fractal for the GBFV homomorphic encryption scheme, including a novel method for making homomorphic NTT evaluation packing-friendly by computing it in two dimensions. Furthermore, we make the proofs publicly verifiable by appending a zero-knowledge Proof of Decryption (PoD). We propose a new construction for PoDs optimized for low proof generation time, exploiting modulus and ring switching in GBFV and using the Schwartz-Zippel lemma for proof batching; these techniques might be of independent interest. Finally, we implement the latter protocol in C and report on execution time and proof sizes.

In planning for PDF-3, the International Centre for Diffraction Data's full pattern database of raw diffraction data, it is evident that a standard format for storage and exchange of diffraction data is necessary. An evaluation of the JCAMP-DX protocol by a task group* of the International Centre for Diffraction Data has resulted in a set of format cooes specific to X-ray diffraction. The proposed structure of the data is divided into four parts: the minimal component set required by the JCAMP-DX definition (name, data, owner, sample identification, data type, etc.), a minimum item set required to define the X-ray diffraction data, an open selection of requested but not required information on the sample, its preparation and the instrument, and finally the data itself in one of several specified formats. All information stored in JCAMP-DX format is in ASCII characters. Therefore, these data are printable, easily read by the user and compatible with almost any computer or media storage device. Codes defining the information are primarily in shortened, but readable, English. The task group is completing the work on this project and will be presenting its proposals to JCAMP.

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

Cryptography is one of the most important approaches to keep digital communication in lock and key and therefore guarantees the privacy, integrity and authenticity of the data by means of complex coding. Cryptographic techniques have arisen centuries ago and the old techniques have continued to evolve as today’s challenges are cloud computing, data storage and retrieval as well as authenticated users. With the widespread spread of cloud environments, trust-enabling systems with cutting-edge technologies such as Zero Knowledge Proofs and blockchain emerged to increase privacy and security. By examining the notion of trust localization, this study draws attention to a use of Gateways as access control points to sensitive information reducing the dependence on the central cloud infrastructures. Additionally, source authentication and authorization provided by Acaras and PKI is studied regarding their integration and effectiveness. The research examines the current challenges in cryptographic security and current findings of cryptographic security and investigates what the future of cryptographic security to build trust and security in modern digital systems..

With the evolving of high-throughput experimental techniques, large amounts of molecular profiling data are becoming available for regular clinical studies. These data need to be stored, processed, archived, distributed and, more importantly, linked. In ELIXIR pilot project, we focus on connecting the archival storage of such data, with databases that store the processed data and visualised workflow systems that manage the computational pipelines. After perusing the processed data, users often come back to the raw data not only to reconfirm the data processing but also to further explore the raw data in workflow systems like Galaxy. In the Translational Research IT (TraIT) project of Center for Translational Molecular Medicine, raw data are stored in European Genome-phenome Archive (EGA); and processed data, tranSMART. In light of the distinct interpretations of data ontology structures in different repositories, we are aiming to establish the a systematic, flexible and sustainable mapping between the processed data and the corresponding raw data regardless of their repositories. We propose an ontology structure with a few stable mapping concepts — Study, Sample Collected Experimental Data (SCED) and File — to connect different repositories, separating the three essential processes: data uploading, data ontology structuring and data retrieving, in such a way that a general upload and structuring methodology can be used for malleable data retrieving. To demonstrate this ontology structure, we create a RDF graph demo using CTMM-TraIT Cell Line Use Case (CLUC) data.

Motivated by the extended deployment of authenticated data structures (e.g., Merkle Patricia Tries) for verifying massive amounts of data in blockchain systems, we begin a systematic study of the I/O efficiency of such systems. We first explore the fundamental limitations of memory checking, a previously-proposed abstraction for verifiable storage, in terms of its locality-a complexity measure that we introduce for the first time and is defined as the number of non-contiguous memory regions a checker must query to verifiably answer a read or a write query. Our central result is an Ω(log n/log log n) lower bound for the locality of any memory checker. Then we turn our attention to (dense and sparse) Merkle trees, one of the most celebrated memory checkers, and provide stronger lower bounds for their locality. For example, we show that any dense Merkle tree layout will have average locality at least (1/3)log n. Furthermore, if we allow node duplication, we show that if any write operation has at most polylog complexity, then the read locality cannot be less than log n/log log n. Our lower bounds help us construct two new locality-optimized authenticated data structures (DupTree and PrefixTree) which we implement and evaluate on random operations and real workloads, and which are shown to outperform traditional Merkle trees, especially as the number of leaves increases.

Cloud computing is getting increasingly popular, but has yet to be widely adopted arguably because there are many security and privacy problems that have not been adequately addressed. A specific problem encountered in the context of cloud storage, where clients outsource their data (files) to untrusted cloud storage servers, is to convince the clients that their data are kept intact at the storage servers. An important approach to achieve this goal is called Proof of Retrievability (POR), by which a storage server can convince a client --- via a concise proof --- that its data can be recovered. However, existing POR solutions can only deal with static data (i.e., data items must be fixed), and actually are not secure when used to deal with dynamic data (i.e., data items need be inserted, deleted, and modified). Motivated by the need to securely deal with dynamic data, we propose the first dynamic POR scheme for this purpose. Moreover, we introduce a new property, called fairness, which is necessary and also inherent to the setting of dynamic data because, without ensuring it, a dishonest client could legitimately accuse an honest cloud storage server of manipulating its data. Our solution is based on two new tools, one is an authenticated data structure we call range-based 2-3 trees (rb23Tree for short), and the other is an incremental signature scheme we call hash-compress-and-sign (HCS for short). These tools might be of independent value as well.