Dynamic Merkle B-Tree with Efficient Proofs
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Angela: a Sparse, Distributed, and Highly Concurrent Merkle Tree
Angela: A Sparse, Distributed, and Highly Concurrent Merkle Tree Janakirama Kalidhindi, Alex Kazorian, Aneesh Khera, Cibi Pari Abstract— Merkle trees allow for efficient and authen- with high query throughput. Any concurrency overhead ticated verification of data contents through hierarchical should not destroy the system’s performance when the cryptographic hashes. Because the contents of parent scale of the merkle tree is very large. nodes in the trees are the hashes of the children nodes, Outside of Key Transparency, this problem is gen- concurrency in merkle trees is a hard problem. The eralizable to other applications that have the need current standard for merkle tree updates is a naive locking of the entire tree as shown in Google’s merkle tree for efficient, authenticated data structures, such as the implementation, Trillian. We present Angela, a concur- Global Data Plane file system. rent and distributed sparse merkle tree implementation. As a result of these needs, we have developed a novel Angela is distributed using Ray onto Amazon EC2 algorithm for updating a batch of updates to a merkle clusters and uses Amazon Aurora to store and retrieve tree concurrently. Run locally on a MacBook Pro with state. Angela is motivated by Google Key Transparency a 2.2GHz Intel Core i7, 16GB of RAM, and an Intel and inspired by it’s underlying merkle tree, Trillian. Iris Pro1536 MB, we achieved a 2x speedup over the Like Trillian, Angela assumes that a large number of naive algorithm mentioned earlier. We expanded this its 2256 leaves are empty and publishes a new root after algorithm into a distributed merkle tree implementation, some amount of time. -
Enabling Authentication of Sliding Window Queries on Streams
ProofInfused Streams: Enabling Authentication of Sliding Window Queries On Streams Feifei Li† Ke Yi‡ Marios Hadjieleftheriou‡ George Kollios† †Computer Science Department Boston University ‡AT&T Labs Inc. lifeifei, gkollios @cs.bu.edu yike, marioh @research.att.com { } { } ABSTRACT As computer systems are essential components of many crit- ical commercial services, the need for secure online transac- . tions is now becoming evident. The demand for such appli- cations, as the market grows, exceeds the capacity of individ- 0110..011 ual businesses to provide fast and reliable services, making Provider Servers outsourcing technologies a key player in alleviating issues Clients of scale. Consider a stock broker that needs to provide a real-time stock trading monitoring service to clients. Since Figure 1: The outsourced stream model. the cost of multicasting this information to a large audi- ence might become prohibitive, the broker could outsource providers, who would be in turn responsible for forwarding the stock feed to third-party providers, who are in turn re- the appropriate sub-feed to clients (see Figure 1). Evidently, sponsible for forwarding the appropriate sub-feed to clients. especially in critical applications, the integrity of the third- Evidently, in critical applications the integrity of the third- party should not be taken for granted, as the latter might party should not be taken for granted. In this work we study have malicious intent or might be temporarily compromised a variety of authentication algorithms for selection and ag- by other malicious entities. Deceiving clients can be at- gregation queries over sliding windows. Our algorithms en- tempted for gaining business advantage over the original able the end-users to prove that the results provided by the service provider, for competition purposes against impor- third-party are correct, i.e., equal to the results that would tant clients, or for easing the workload on the third-party have been computed by the original provider. -
A Genetic Algorithm for Database Index Selection
GADIS: A Genetic Algorithm for Database Index Selection Priscilla Neuhaus, Julia Couto, Jonatas Wehrmann, Duncan D. Ruiz and Felipe Meneguzzi School of Technology, PUCRS - Pontifícia Universidade Católica do Rio Grande do Sul - Porto Alegre, Brazil Email: [priscilla.neuhaus, julia.couto, jonatas.wehrmann]@edu.pucrs.br, [duncan.ruiz, felipe.meneguzzi]@pucrs.br Abstract—Creating an optimal amount of indexes, taking optimize the query response time by search for faster index into account query performance and database size remains a configurations when compared to the initial one. challenge. In theory, one can speed up query response by creating We perform a set of experiments using TPC-H, a well indexes on the most used columns, although causing slower data insertion and deletion, and requiring a much larger amount of known database benchmark [11], and we compare our results memory for storing the indexing data, but in practice, it is very with three baseline approaches: (i) the initial index configura- important to balance such a trade-off. This is not a trivial task tion; and (ii) indexes derived from a random-search algorithm. that often requires action from the Database Administrator. We Results show that GADIS outperforms the baselines, allowing address this problem by introducing GADIS, A Genetic Algorithm for better index configuration on the optimized database. for Database Index Selection, designed to automatically select the best configuration of indexes adaptable for any database Finally, we observe that our approach is much more prone schema. This method aims to find the fittest individuals for to find a proper solution that the competitive methods. -
Providing Authentication and Integrity in Outsourced Databases Using Merkle Hash Tree’S
Providing Authentication and Integrity in Outsourced Databases using Merkle Hash Tree's Einar Mykletun Maithili Narasimha University of California Irvine University of California Irvine [email protected] [email protected] Gene Tsudik University of California Irvine [email protected] 1 Introduction In this paper we intend to describe and summarize results relating to the use of authenticated data structures, specifically Merkle Hash Tree's, in the context of the Outsourced Database Model (ODB). In ODB, organizations outsource their data management needs to an external untrusted service provider. The service provider hosts clients' databases and offers seamless mechanisms to create, store, update and access (query) their databases. Due to the service provider being untrusted, it becomes imperative to provide means to ensure authenticity and integrity in the query replies returned by the provider to clients. It is therefore the goal of this paper to outline an applicable data structure that can support authenticated query replies from the service provider to the clients (who issue the queries). Merkle Hash Tree's (introduced in section 3) is such a data structure. 1.1 Outsourced Database Model (ODB) The outsourced database model (ODB) is an example of the well-known Client-Server model. In ODB, the Database Service Provider (also referred to as the Server) has the infrastructure required to host outsourced databases and provides efficient mechanisms for clients to create, store, update and query the database. The owner of the data is identified as the entity who has to access to add, remove, and modify tuples in the database. The owner and the client may or may not be the same entity. -
The SCADS Toolkit Nick Lanham, Jesse Trutna
The SCADS Toolkit Nick Lanham, Jesse Trutna (it’s catching…) SCADS Overview SCADS is a scalable, non-relational datastore for highly concurrent, interactive workloads. Scale Independence - as new users join No changes to application Cost per user doesn’t increase Request latency doesn’t change Key Innovations 1. Performance safe query language 2. Declarative performance/consistency tradeoffs 3. Automatic scale up and down using machine learning Toolkit Motivation Investigated other open-source distributed key-value stores Cassandra, Hypertable, CouchDB Monolithic, opaque point solutions Make many decisions about how to architect the system a -prori Want set of components to rapidly explore the space of systems’ design Extensible components communicate over established APIs Understand the implications and performance bottlenecks of different designs SCADS Components Component Responsibilities Storage Layer Persist and serve data for a specified key responsibility Copy and sync data between storage nodes Data Placement Layer Assign node key responsibilities Manage replication and partition factors Provide clients with key to node mapping Provides mechanism for machine learning policies Client Library Hides client interaction with distributed storage system Provides higher-level constructs like indexes and query language Storage Layer Key-value store that supports range queries built on BDB API Record get(NameSpace ns, RecordKey key) list<Record> get_set (NameSpace ns, RecordSet rs ) bool put(NameSpace ns, Record rec) i32 count_set(NameSpace ns, RecordSet rs) bool set_responsibility_policy(NameSpace ns,RecordSet policy) RecordSet get_responsibility_policy(NameSpace ns) bool sync_set(NameSpace ns, RecordSet rs, Host h, ConflictPolicy policy) bool copy_set(NameSpace ns, RecordSet rs, Host h) bool remove_set(NameSpace ns, RecordSet rs) Storage Layer Storage Layer Storage Layer: Synchronize Replicas may diverge during network partitions (in order to preserve availability). -
Authentication of Moving Range Queries
Authentication of Moving Range Queries Duncan Yung Eric Lo Man Lung Yiu ∗ Department of Computing Hong Kong Polytechnic University {cskwyung, ericlo, csmlyiu}@comp.polyu.edu.hk ABSTRACT the user leaves the safe region, Thus, safe region is a powerful op- A moving range query continuously reports the query result (e.g., timization for significantly reducing the communication frequency restaurants) that are within radius r from a moving query point between the user and the service provider. (e.g., moving tourist). To minimize the communication cost with The query results and safe regions returned by LBS, however, the mobile clients, a service provider that evaluates moving range may not always be accurate. For instance, a hacker may infiltrate queries also returns a safe region that bounds the validity of query the LBS’s servers [25] so that results of queries all include a partic- results. However, an untrustworthy service provider may report in- ular location (e.g., the White House). Furthermore, the LBS may be correct safe regions to mobile clients. In this paper, we present effi- self-compromised and thus ranks sponsored facilities higher than cient techniques for authenticating the safe regions of moving range actual query result. The LBS may also return an overly large safe queries. We theoretically proved that our methods for authenticat- region to the clients for the sake of saving computing resources and ing moving range queries can minimize the data sent between the communication bandwidth [22, 16, 30]. On the other hand, the LBS service provider and the mobile clients. Extensive experiments are may opt to return overly small safe regions so that the clients have carried out using both real and synthetic datasets and results show to request new safe regions more frequently, if the LBS charges fee that our methods incur small communication costs and overhead. -
Response Time Analysis on Indexing in Relational Databases and Their Impact
Response time analysis on indexing in relational databases and their impact Sam Pettersson & Martin Borg 18, June, 2020 Dept. Computer Science & Engineering Blekinge Institute of Technology SE–371 79 Karlskrona, Sweden This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the bachelor’s degree in software engineering. The thesis is equivalent to 10 weeks of full-time studies. Contact Information: Authors: Martin Borg E-mail: [email protected] Sam Pettersson E-mail: [email protected] University advisor: Associate Professor. Mikael Svahnberg Dept. Computer Science & Engineering Faculty of Computing Internet : www.bth.se Blekinge Institute of Technology Phone : +46 455 38 50 00 SE–371 79 Karlskrona, Sweden Fax : +46 455 38 50 57 Abstract This is a bachelor thesis concerning the response time and CPU effects of indexing in relational databases. Analyzing two popular databases, PostgreSQL and MariaDB with a real-world database structure us- ing randomized entries. The experiment was conducted with Docker and its command-line interface without cached values to ensure fair outcomes. The procedure was done throughout seven different create, read, update and delete queries with multiple volumes of database en- tries to discover their strengths and weaknesses when utilizing indexes. The results indicate that indexing has an overall enhancing effect on almost all of the queries. It is found that join, update and delete op- erations benefits the most from non-clustered indexing. PostgreSQL gains the most from indexes while MariaDB has less of an improvement in the response time reduction. -
Relational Database Index Design
RReellaattiioonnaall DDaattaabbaassee IInnddeexx DDeessiiggnn Prerequisite for Index Design Workshop Tapio Lahdenmaki April 2004 Copyright Tapio Lahdenmaki, DBTech Pro 1 Relational Database Index Design CHAPTER 1: INTRODUCTION........................................................................................................................4 INADEQUATE INDEXING .......................................................................................................................................4 THE IMPACT OF HARDWARE EVOLUTION ............................................................................................................5 Volatile Columns Should Not Be Indexed – True Or False? ..........................................................................7 Example ..........................................................................................................................................................7 Disk Drive Utilisation.....................................................................................................................................8 SYSTEMATIC INDEX DESIGN ................................................................................................................................9 CHAPTER 2........................................................................................................................................................13 TABLE AND INDEX ORGANIZATION ........................................................................................................13 INTRODUCTION -
IBM DB2 for I Indexing Methods and Strategies Learn How to Use DB2 Indexes to Boost Performance
IBM DB2 for i indexing methods and strategies Learn how to use DB2 indexes to boost performance Michael Cain Kent Milligan DB2 for i Center of Excellence IBM Systems and Technology Group Lab Services and Training July 2011 © Copyright IBM Corporation, 2011 Table of contents Abstract........................................................................................................................................1 Introduction .................................................................................................................................1 The basics....................................................................................................................................3 Database index introduction .................................................................................................................... 3 DB2 for i indexing technology .................................................................................................................. 4 Radix indexes.................................................................................................................... 4 Encoded vector indexes .................................................................................................... 6 Bitmap indexes - the limitations ....................................................................................................... 6 EVI structure details......................................................................................................................... 7 EVI runtime usage........................................................................................................................... -
Executing Large Scale Processes in a Blockchain
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2514-4774.htm JCMS 2,2 Executing large-scale processes in a blockchain Mahalingam Ramkumar Department of Computer Science and Engineering, Mississippi State University, 106 Starkville, Mississippi, USA Received 16 May 2018 Revised 16 May 2018 Abstract Accepted 1 September 2018 Purpose – The purpose of this paper is to examine the blockchain as a trusted computing platform. Understanding the strengths and limitations of this platform is essential to execute large-scale real-world applications in blockchains. Design/methodology/approach – This paper proposes several modifications to conventional blockchain networks to improve the scale and scope of applications. Findings – Simple modifications to cryptographic protocols for constructing blockchain ledgers, and digital signatures for authentication of transactions, are sufficient to realize a scalable blockchain platform. Originality/value – The original contributions of this paper are concrete steps to overcome limitations of current blockchain networks. Keywords Cryptography, Blockchain, Scalability, Transactions, Blockchain networks Paper type Research paper 1. Introduction A blockchain broadcast network (Bozic et al., 2016; Croman et al., 2016; Nakamoto, 2008; Wood, 2014) is a mechanism for creating a distributed, tamper-proof, append-only ledger. Every participant in the broadcast network maintains a copy, or some representation, of the ledger. Ledger entries are made by consensus on the states of a process “executed” on the blockchain. As an example, in the Bitcoin (Nakamoto, 2008) process: (1) Bitcoin transactions transfer Bitcoins from a wallet to one or more other wallets. (2) Bitcoins created by “mining” are added to the miner’s wallet. -
IB Case Study Vocabulary a Local Economy Driven by Blockchain (2020) Websites
IB Case Study Vocabulary A local economy driven by blockchain (2020) Websites Merkle Tree: https://blockonomi.com/merkle-tree/ Blockchain: https://unwttng.com/what-is-a-blockchain Mining: https://www.buybitcoinworldwide.com/mining/ Attacks on Cryptocurrencies: https://blockgeeks.com/guides/hypothetical-attacks-on-cryptocurrencies/ Bitcoin Transaction Life Cycle: https://ducmanhphan.github.io/2018-12-18-Transaction-pool-in- blockchain/#transaction-pool 51 % attack - a potential attack on a blockchain network, where a single entity or organization can control the majority of the hash rate, potentially causing a network disruption. In such a scenario, the attacker would have enough mining power to intentionally exclude or modify the ordering of transactions. Block - records, which together form a blockchain. ... Blocks hold all the records of valid cryptocurrency transactions. They are hashed and encoded into a hash tree or Merkle tree. In the world of cryptocurrencies, blocks are like ledger pages while the whole record-keeping book is the blockchain. A block is a file that stores unalterable data related to the network. Blockchain - a data structure that holds transactional records and while ensuring security, transparency, and decentralization. You can also think of it as a chain or records stored in the forms of blocks which are controlled by no single authority. Block header – main way of identifying a block in a blockchain is via its block header hash. The block hash is responsible for block identification within a blockchain. In short, each block on the blockchain is identified by its block header hash. Each block is uniquely identified by a hash number that is obtained by double hashing the block header with the SHA256 algorithm. -
Efficient Verification of Shortest Path Search Via Authenticated Hints Man Lung YIU Hong Kong Polytechnic University
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Institutional Knowledge at Singapore Management University Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 3-2010 Efficient Verification of Shortest Path Search Via Authenticated Hints Man Lung YIU Hong Kong Polytechnic University Yimin LIN Singapore Management University, [email protected] Kyriakos MOURATIDIS Singapore Management University, [email protected] DOI: https://doi.org/10.1109/ICDE.2010.5447914 Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research Part of the Databases and Information Systems Commons, and the Numerical Analysis and Scientific omputC ing Commons Citation YIU, Man Lung; LIN, Yimin; and MOURATIDIS, Kyriakos. Efficient Verification of Shortest Path Search Via Authenticated Hints. (2010). ICDE 2010: IEEE 26th International Conference on Data Engineering: Long Beach, California, USA, 1 - 6 March 2010. 237-248. Research Collection School Of Information Systems. Available at: https://ink.library.smu.edu.sg/sis_research/507 This Conference Proceeding Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at Singapore Management University.