DOCSLIB.ORG

Authenticated Data Structures, Generically

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Authenticated Data Structures, Generically Authenticated Data Structures, Generically Andrew Miller, Michael Hicks, Jonathan Katz, and Elaine Shi University of Maryland, College Park, USA Abstract Such a scenario can be supported using authenticated data An authenticated data structure (ADS) is a data structure whose structures (ADS) [5, 23, 30]. ADS computations involve two roles, operations can be carried out by an untrusted prover, the results of the prover and the verifier. The mirror plays the role of the prover, which a verifier can efficiently check as authentic. This is done storing the data of interest and answering queries about it. The by having the prover produce a compact proof that the verifier client plays the role of the verifier, posing queries to the prover can check along with each operation’s result. ADSs thus support and verifying that the returned results are authentic. At any point outsourcing data maintenance and processing tasks to untrusted in time, the verifier holds only a short digest that can be viewed as servers without loss of integrity. Past work on ADSs has focused summarizing the current contents of the data; an authentic copy of on particular data structures (or limited classes of data structures), the digest is provided by the data owner. When the verifier sends one at a time, often with support only for particular operations. the prover a query, the prover computes the result and returns it This paper presents a generic method, using a simple exten- along with a proof that the returned result is correct; both the proof sion to a ML-like functional programming language we call λ• and the time to produce it are linear in the time to compute the (lambda-auth), with which one can program authenticated oper- query result. The verifier can attempt to verify the proof (in time ations over any data structure defined by standard type construc- linear in the size of the proof) using its current digest, and will tors, including recursive types, sums, and products. The program- accept the returned result only if the proof verifies. If the verifier is mer writes the data structure largely as usual and it is compiled to also the data provider, the verifier may also update its data stored code to be run by the prover and verifier. Using a formalization of at the prover; in this case, the result is an updated digest and the λ• we prove that all well-typed λ• programs result in code that is proof shows that this updated digest was computed correctly. ADS secure under the standard cryptographic assumption of collision- computations have two properties. Correctness implies that when resistant hash functions. We have implemented λ• as an extension both parties execute the protocol correctly, the proofs given by the prover verify correctly and the verifier always receives the correct to the OCaml compiler, and have used it to produce authenticated 1 versions of many interesting data structures including binary search result. Security implies that a computationally bounded, malicious trees, red-black+ trees, skip lists, and more. Performance experi- prover cannot fool the verifier into accepting an incorrect result. ments show that our approach is efficient, giving up little compared Authenticated data structures can be traced back to Merkle [18]; to the hand-optimized data structures developed previously. the well-known Merkle hash tree can be viewed as providing an authenticated version of a bounded-length array. More recently, au- Categories and Subject Descriptors D.3.3 [Programming Lan- thenticated versions of data structures as diverse as sets [22, 26], guages]: Language Constructs and Features—Data types and struc- dictionaries [1, 12], range trees [16], graphs [13], skip lists [11, 12], tures B-trees [20], hash trees [25], and more [15] have been proposed. In each of these cases, the design of the data structure, the supporting General Terms Security, Programming Languages, Cryptogra- operations, and how they can be proved authentic have been recon- phy sidered from scratch, involving a new, potentially tricky proof of security. Arguably, this state of affairs has hindered the advance- 1. Introduction ment of new data-structure designs as previous ideas are not easily reused or reapplied. We believe that ADSs will make their way into Suppose data provider would like to allow third parties to mirror its systems more often if they become easier to build. data, providing a query interface over it to clients. The data provider This paper presents λ• (pronounced “lambda auth”), a language wants to assure clients that the mirrors will answer queries over the for programming authenticated data structures. λ• represents the data truthfully, even if they (or another party that compromises a first generic, language-based approach to building dynamic authen- mirror) have an incentive to lie. As examples, the data provider ticated data structures with provable guarantees. The key observa- might be providing stock market data, a certificate revocation list, tion underlying λ•’s design is that, whatever the data structure or the Tor relay list, or the state of the current Bitcoin ledger [21]. operation, the computations performed by the prover and verifier can be made structurally the same: the prover constructs the proof Permission to make digital or hard copies of all or part of this work for personal or at key points when executing a query, and the verifier checks a proof classroom use is granted without fee provided that copies are not made or distributed by using it to “replay” the query, checking at each key point that the for profit or commercial advantage and that copies bear this notice and the full citation computation is self-consistent. on the first page. Copyrights for components of this work owned by others than the λ• authenticated types author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or implements this idea using what we call , republish, to post on servers or to redistribute to lists, requires prior specific permission written •τ, with coercions auth and unauth for introducing and and/or a fee. Request permissions from [email protected]. eliminating values of an authenticated type. Using standard func- POPL ’14, January 22–24, 2014, San Diego, CA, USA. Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-2544-8/14/01. $15.00. 1 This property is sometimes called soundness but we eschew this term to http://dx.doi.org/10.1145/2535838.2535851 avoid confusion with its standard usage in programming languages. tional programming features, the programmer writes her ADS’s N2,0: h1 = hash(h2,h3) π = str2 @ N0,2 :: datatype definition and its corresponding operations (e.g., queries h7 @ N0,3 :: and updates) to use authenticated types. For example, as we show N1,0: h2 = hash(h4,h5) N1,1: h3 = hash(h6,h7) h2 @ N1,0 later in the paper, the programmer could write an efficient authen- ticated binary search tree using the (OCaml-style) type definition N0,0: str0, h4 N0,1: str1, h5 N0,2: str2, h6 N0,3: str3, h7 type bst = Tip j Bin of •bst × int × •bst along with es- h4 = hash(str0) h5 = hash(str1) h6 = hash(str2) h7 = hash(str3) sentially standard routines for querying and insertion. Then, given such a program, the λ• compiler produces code for both a prover Figure 1. A Merkle tree and proof π for fetch(2) describing the and a verifier that will produce or confirm, respectively, a proof of highlighted path (with bold edge and node outlines). the correct execution of the corresponding operation. Proofs con- sist of a stream of what we call shallow projections of the data the prover visits while running its routine: the prover’s code adds 2. We formalize the semantics and type rules for λ• and prove to this stream at each unauth call, while the verifier’s code draws that all well-typed λ• programs produce ADS protocols that from the stream at the corresponding call, checking for consistency. are both correct and secure. We give a more detailed overview of how this approach works, and 3. We have implemented λ• as an extension to the OCaml com- how authenticated types are represented, in Section 2. Importantly, piler3 and used it to program a variety of existing and new as we show in Sections 3 and 4, any well-typed program written in ADSs, showing good asymptotic performance that is compet- λ• compiles to a prover and verifier which are correct and secure, itive with hand-rolled implementations. where security holds under the standard cryptographic assumption 2 of collision-resistant hash functions. 2. Overview λ• provides two key benefits over prior work. First, it is ex- tremely flexible. We can use λ• to implement any dynamic data This section presents an overview of our approach. We begin by structure, both queries and updates, expressible using ML-style describing Merkle trees, the canonical example of an authenticated data types (involving sums, products, and recursive types). Our data structure. Then we give a flavor for λ• by showing how we can theoretical development, though not our implementation, also sup- use it to implement Merkle trees. We conclude with a discussion of ports authenticated functions. Previous work by Martel et al. [17] the flexibility and ease-of-use benefits of using λ• to write efficient can also be used to build DAG-oriented ADSs, but it supports only authenticated data structures. queries and not (incremental) updates, requires the data structure have a single root, and does not support authenticated functions. 2.1 Background: Merkle trees λ•’s flexibility does not compromise its performance. To the best The canonical example of an ADS is a Merkle tree [19], which is of our knowledge the asymptotic performance of every prior ADS the authenticated version of a full binary tree where data is stored construction from the literature based on collision-resistant hash- at the leaves but not the interior nodes.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Redactable Hashing for Time Verification
    Technical Disclosure Commons Defensive Publications Series January 2021 Redactable Hashing for Time Verification Jan Schejbal Follow this and additional works at: https://www.tdcommons.org/dpubs_series Recommended Citation Schejbal, Jan, "Redactable Hashing for Time Verification", Technical Disclosure Commons, (January 24, 2021) https://www.tdcommons.org/dpubs_series/3999 This work is licensed under a Creative Commons Attribution 4.0 License. This Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons. Schejbal: Redactable Hashing for Time Verification REDACTABLE HASHING FOR TIME VERIFICATION Cryptographic hashes (outputs of cryptographically secure hash functions) are used to uniquely identify secret information without making such information public. A cryptographic hash function is a deterministic computation that produces the same outputs for the same inputs while ensuring that even a small change of the input (e.g., a change of only one or several bits) results in an unpredictable output that has little resemblance to the original (unmodified) input. Furthermore, it is not possible to efficiently find an input matching a certain hash value or two different inputs having the same hash value; an attacker that attempts to do so would need to test numerous values until one matches, which is computationally infeasible for a sufficiently large space of possible values. The quasi-randomness property of hashes makes them useful for mapping protected information while ensuring that the information is not easily discoverable by a malicious attacker or an unauthorized viewer. Hashes can be used to derive cryptographic keys from other (strong or even weak) cryptographic material.
    [Show full text]
  • "On Merkle Trees", Dr Craig S Wright
    On Merkle Trees Dr Craig S Wright By now, it should be well understood that Bitcoin utilises the concept of a Merkle tree to its advantage; by way of background, we provide an explanation in this post. The following aids as a technical description and definition of similar concepts used more generally in Bitcoin and specifically within SPV. Merkle trees are hierarchical data structures that enable secure verification of collections of data. In a Merkle tree, each node in the tree has been given an index pair (i, j) and is represented as N(i, j). The indices i, j are simply numerical labels that are related to a specific position in the tree. An important feature of the Merkle tree is that the construction of each of its nodes is governed by the following (simplified) equations: 퐻(퐷 ) 푖 = 푗 푁(푖, 푗) = { 푖 , 퐻(푁(푖, 푘) ∥ 푁(푘 + 1, 푗)) 푖 ≠ 푗 where 푘 = (푖 + 푗 − 1)⁄2 and H is a cryptographic hash function. A labelled, binary Merkle tree constructed according to the equations is shown in figure 1, from which we can see that the i = j case corresponds to a leaf node, which is simply the hash of the th corresponding i packet of data Di. The 푖 ≠ 푗 case corresponds to an internal or parent node, which is generated by recursively hashing and concatenating child nodes, until one parent (the Merkle root) is found. Figure 1. A Merkle tree. For example, the node 푁(1,4) is constructed from the four data packets 퐷1, … , 퐷4 as 푁(1,4) = 퐻(푁(1,2) ∥ 푁(3,4)) .
    [Show full text]
  • Practical Authenticated Pattern Matching with Optimal Proof Size
    Practical Authenticated Pattern Matching with Optimal Proof Size Dimitrios Papadopoulos Charalampos Papamanthou Boston University University of Maryland [email protected] [email protected] Roberto Tamassia Nikos Triandopoulos Brown University RSA Laboratories & Boston University [email protected] [email protected] ABSTRACT otherwise. More elaborate models for pattern matching involve We address the problem of authenticating pattern matching queries queries expressed as regular expressions over Σ or returning multi- over textual data that is outsourced to an untrusted cloud server. By ple occurrences of p, and databases allowing search over multiple employing cryptographic accumulators in a novel optimal integrity- texts or other (semi-)structured data (e.g., XML data). This core checking tool built directly over a suffix tree, we design the first data-processing problem has numerous applications in a wide range authenticated data structure for verifiable answers to pattern match- of topics including intrusion detection, spam filtering, web search ing queries featuring fast generation of constant-size proofs. We engines, molecular biology and natural language processing. present two main applications of our new construction to authen- Previous works on authenticated pattern matching include the ticate: (i) pattern matching queries over text documents, and (ii) schemes by Martel et al. [28] for text pattern matching, and by De- exact path queries over XML documents. Answers to queries are vanbu et al. [16] and Bertino et al. [10] for XML search. In essence, verified by proofs of size at most 500 bytes for text pattern match- these works adopt the same general framework: First, by hierarchi- ing, and at most 243 bytes for exact path XML search, indepen- cally applying a cryptographic hash function (e.g., SHA-2) over the dently of the document or answer size.
    [Show full text]
  • A New Blockchain-Based Multi-Level Location Secure Sharing Scheme
    applied sciences Article A New Blockchain-Based Multi-Level Location Secure Sharing Scheme Qiuhua Wang 1,* , Tianyu Xia 1, Yizhi Ren 1, Lifeng Yuan 1 and Gongxun Miao 2 1 School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China; [email protected] (T.X.); [email protected] (Y.R.); [email protected] (L.Y.) 2 Zhongfu Information Co., Ltd., Jinan 250101, China; [email protected] * Correspondence: [email protected]; Tel.: +86-0571-868-73820 Abstract: Currently, users’ location information is collected to provide better services or research. Using a central server to collect, store and share location information has inevitable defects in terms of security and efficiency. Using the point-to-point sharing method will result in high computation overhead and communication overhead for users, and the data are hard to be verified. To resolve these issues, we propose a new blockchain-based multi-level location secure sharing scheme. In our proposed scheme, the location data are set hierarchically and shared with the requester according to the user’s policy, while the received data can be verified. For this, we design a smart contract to implement attribute-based access control and establish an incentive and punishment mechanism. We evaluate the computation overhead and the experimental results show that the computation overhead of our proposed scheme is much lower than that of the existing scheme. Finally, we analyze the performances of our proposed scheme and demonstrate that our proposed scheme is, overall, better than existing schemes. Keywords: location sharing; privacy preserving; blockchain; Merkle tree; smart contract; attribute- Citation: Wang, Q.; Xia, T.; Ren, Y.; based access control Yuan, L.; Miao, G.
    [Show full text]