An online backup system should be quick and reliable in both saving and restoring users' data. To do so in a peer-to-peer implementation, data transfer scheduling and the amount of redundancy must be chosen wisely. We formalize the problem of exchanging multiple pieces of data with intermittently available peers, and we show that random scheduling completes transfers nearly optimally in terms of duration as long as the system is sufficiently large. Moreover, we propose an adaptive redundancy scheme that improves performance and decreases resource usage while keeping the risks of data loss low. Extensive simulations show that our techniques are effective in a realistic trace-driven scenario with heterogeneous bandwidth.
Backup objects, whose confidentiality can be ensured by standard encryption techniques, should encode incremental differences between archive versions. Recently, various techniques have been proposed to optimize computational time and size of these differences @cite_3 .
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When persistent storage fails, traditional media recovery first restores an old backup image followed by replaying the recovery log since the last backup operation. Restoring a backup can take hours, but log replay often takes much longer due to its random access pattern. We introduce single-pass restore, a technique in which restoration of all backups and log replay are performed in a single operation. This allows hiding log replay within the initial restore of the backup, thus substantially reducing the time and cost of media recovery and, incidentally, rendering incremental backup techniques unnecessary. Single-pass restore is enabled by a new organization of the log archive, created by a continuous process that is easily incorporated into the traditional log archiving process. Our empirical analysis shows that the imposed overhead is negligible in comparison with the substantial benefits provided. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Representing versioned documents, such as Wikipedia history, web archives, genome databases, backups, is challenging when we want to support searching for an exact substring and retrieve the documents that contain the substring. This problem is called document listing. We present an index for the document listing problem on versioned documents. Our index is the first one based on grammar-compression. This allows for good results on repetitive collections, whereas standard techniques cannot achieve competitive space for solving the same problem. Our index can also be addapted to work in a more standard way, allowing users to search for word-based phrase queries and conjunctive queries at the same time. Finally, we discuss extensions that may be possible in the future, for example, supporting ranking capabilities within the index itself. We study the problem of creating highly compressed full-text index structures for versioned document collections, that is, collections that contain multiple versions of each document. Important examples of such collections are Wikipedia or the web page archive maintained by the Internet Archive. A straightforward indexing approach would simply treat each document version as a separate document, such that index size scales linearly with the number of versions. However, several authors have recently studied approaches that exploit the significant similarities between different versions of the same document to obtain much smaller index sizes. In this paper, we propose new techniques for organizing and compressing inverted index structures for such collections. We also perform a detailed experimental comparison of new techniques and the existing techniques in the literature. Our results on an archive of the English version of Wikipedia, and on a subset of the Internet Archive collection, show significant benefits over previous approaches. A major issue in many applications of Wireless Sensor Networks (WSNs) is ensuring security. Particularly, in military applications, sensors are usually deployed in hostile areas where they can be easily captured and operated by an adversary. Most of security attacks in WSNs are due to the lack of security guaranties in terms of authentication, integrity, and confidentiality. These services are often provided using cryptographic primitives where sensor nodes need to agree on a set of secret keys. Current key distribution schemes are not fully adapted to the tiny, low-cost, and fragile nature of sensors that are equipped with limited computation capability, reduced memory size, and battery-based power supply. This paper investigates the design of an efficient key distribution and management scheme for wireless sensor networks. The proposed scheme can ensure the generation and distribution of different encryption keys intended to secure individual and group communications. This is performed based on elliptic curve public key encryption using Diffie-Hellman like key exchange that is applied at different levels of the network topology. In addition, a re-keying procedure is performed using secret sharing techniques. This scheme is more efficient and less complex than existing approaches, due to the reduced number of messages and the less processing overhead required to accomplish key exchange. Furthermore, few number of encryption keys with reduced sizes are managed in sensor nodes, which optimizes memory usage and enhances scalability to large size networks. Versioned document collections are collections that contain multiple versions of each document. Important examples are Web archives, Wikipedia and other wikis, or source code and documents maintained in revision control systems. Versioned document collections can become very large, due to the need to retain past versions, but there is also a lot of redundancy between versions that can be exploited. Thus, versioned document collections are usually stored using special differential (delta) compression techniques, and a number of researchers have recently studied how to exploit this redundancy to obtain more succinct full-text index structures. In this paper, we study index organization and compression techniques for such versioned full-text index structures. In particular, we focus on the case of positional index structures, while most previous work has focused on the non-positional case. Building on earlier work in [zs:redun], we propose a framework for indexing and querying in versioned document collections that integrates non-positional and positional indexes to enable fast top-k query processing. Within this framework, we define and study the problem of minimizing positional index size through optimal substring partitioning. Experiments on Wikipedia and web archive data show that our techniques achieve significant reductions in index size over previous work while supporting very fast query processing. Many objects, such as files, electronic messages, and web pages, contain overlapping content. Numerous past research projects have observed that one can compress one object relative to another one by computing the differences between the two, but these delta-encoding systems have almost invariably required knowledge of a specific relationship between them—most commonly, two versions using the same name at different points in time. We consider cases in which this relationship is determined dynamically, by efficiently determining when a sufficient resemblance exists between two objects in a relatively large collection. We look at specific examples of this technique, namely web pages, email, and files in a file system, and evaluate the potential data reduction and the factors that influence this reduction. We find that delta-encoding using this resemblance detection technique can improve on simple compression by up to a factor of two, depending on workload, and that a small fraction of objects can potentially account for a large portion of these savings. The importance of cryptography on ensuring security or integrity of the electronic data transaction had become higher during the past few years. Multiple security protocols are currently using various block ciphers. One of the most widely used block ciphers is the Advanced Encryption Standard (AES) which is chosen as a standard for its higher efficiency and stronger security than its competitors. Unfortunately, the encryption and decryption processes of AES takes a considerable amount of time for large data size. The GPU is an attractive platform for accelerating block ciphers and other cryptography algorithms due to its massively parallel processing power. In this work, an implementation of the AES-128 ECB Encryption on three different GPU architectures (Kepler, Maxwell and Pascal) has been presented. The results show that encryption speeds with 207 Gbps on the NVIDIA GTX TITAN X (Maxwell) and 280 Gbps on the NVIDIA GTX 1080 (Pascal) have been achieved by performing new optimization techniques using 32bytes thread granularity. Explosion of data growth and duplication of data in enterprises has led to the deployment of a variety of deduplication technologies. However not all deduplication technologies serve the needs of every workload. Most prior research in deduplication concentrates on fixed block size (or variable block size at a fixed block boundary) deduplication which provides sub-optimal space efficiency in workloads where the duplicate data is not block aligned. Workloads also differ in the nature of operations and their priorities thereby affecting the choice of the right flavor of deduplication. Object workloads for instance, hold multiple versions of archived documents that have a high degree of duplicate data. They are also write-once read-many in nature and follow a whole object GET, PUT and DELETE model and would be better served by a deduplication strategy that takes care of nonblock aligned changes to data. In this paper, we describe and evaluate a hybrid of a variable length and block based deduplication that is hierarchical in nature. We are motivated by the following insights from real world data: (a) object workload applications do not do in-place modification of data and hence new versions of objects are written again as a whole (b) significant amount of data among different versions of the same object is shareable but the changes are usually not block aligned. While the second point is the basis for variable length technique, both the above insights motivate our hierarchical deduplication strategy. We show through experiments with production data-sets from enterprise environments that this provides up to twice the space savings compared to a fixed block deduplication. Encryption ransomware is a malicious software that stealthily encrypts user files and demands a ransom to provide access to these files. Several prior studies have developed systems to detect ransomware by monitoring the activities that typically occur during a ransomware attack. Unfortunately, by the time the ransomware is detected, some files already undergo encryption and the user is still required to pay a ransom to access those files. Furthermore, ransomware variants can obtain kernel privilege, which allows them to terminate software-based defense systems, such as anti-virus. While periodic backups have been explored as a means to mitigate ransomware, such backups incur storage overheads and are still vulnerable as ransomware can obtain kernel privilege to stop or destroy backups. Ideally, we would like to defend against ransomware without relying on software-based solutions and without incurring the storage overheads of backups. To that end, this paper proposes FlashGuard, a ransomware tolerant Solid State Drive (SSD) which has a firmware-level recovery system that allows quick and effective recovery from encryption ransomware without relying on explicit backups. FlashGuard leverages the observation that the existing SSD already performs out-of-place writes in order to mitigate the long erase latency of flash memories. Therefore, when a page is updated or deleted, the older copy of that page is anyway present in the SSD. FlashGuard slightly modifies the garbage collection mechanism of the SSD to retain the copies of the data encrypted by ransomware and ensure effective data recovery. Our experiments with 1,447 manually labeled ransomware samples show that FlashGuard can efficiently restore files encrypted by ransomware. In addition, we demonstrate that FlashGuard has a negligible impact on the performance and lifetime of the SSD. Users are storing ever-increasing amounts of information digitally, driven by many factors including government regulations and the public's desire to digitally record their personal histories. Unfortunately, many of the security mechanisms that modern systems rely upon, such as encryption, are poorly suited for storing data for indefinitely long periods of time--it is very difficult to manage keys and update cryptosystems to provide secrecy through encryption over periods of decades. Worse, an adversary who can compromise an archive need only wait for cryptanalysis techniques to catch up to the encryption algorithm used at the time of the compromise in order to obtain "secure" data. To address these concerns, we have developed POTSHARDS, an archival storage system that provides long-term security for data with very long lifetimes without using encryption. Secrecy is achieved by using provably secure secret splitting and spreading the resulting shares across separately-managed archives. Providing availability and data recovery in such a system can be difficult; thus, we use a new technique, approximate pointers, in conjunction with secure distributed RAID techniques to provide availability and reliability across independent archives. To validate our design, we developed a prototype POTSHARDS implementation, which has demonstrated "normal" storage and retrieval of user data using indexes, the recovery of user data using only the pieces a user has stored across the archives and the reconstruction of an entire failed archive. Although cryptographic techniques are playing an increasingly important role in modern computing system security, user-level tools for encrypting file data are cumbersome and suffer from a number of inherent vulnerabilities. The Cryptographic File System (CFS) pushes encryption services into the file system itself. CFS supports secure storage at the system level through a standard Unix file system interface to encrypted files. Users associate a cryptographic key with the directories they wish to protect. Files in these directories (as well as their pathname components) are transparently encrypted and decrypted with the specified key without further user intervention; cleartext is never stored on a disk or sent to a remote file server. CFS can use any available file system for its underlying storage without modification, including remote file servers such as NFS. System management functions, such as file backup, work in a normal manner and without knowledge of the key. This paper describes the design and implementation of CFS under Unix. Encryption techniques for file system-level encryption are described, and general issues of cryptographic system interfaces to support routine secure computing are discussed. Version control systems are widely used in software development and document management. Unfortunately, versioning confidential files is not normally supported: Existing solutions encrypt the transport channel, but store data in plaintext within a repository. We come up with an access control solution that allows secure versioning of confidential files even in the presence of a malicious server administrator. Using convergent encryption as a building block, we enable space-efficient storage of version histories despite secure encryption. We describe an implementation of our concept for the Subversion (SVN) system, and evaluate storage efficiency and runtime of this implementation. Our implementation is compatible with existing SVN versions without requiring changes to the storage backend. In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset. Data security in the cloud is a big concern that blocks the widespread use of the cloud for relational data management. First, to ensure data security, data confidentiality needs to be provided when data resides in storage as well as when data is dynamically accessed by queries. Prior works on query processing on encrypted data did not provide data confidentiality guarantees in both aspects. Tradeoff between secrecy and efficiency needs to be made when satisfying both aspects of data confidentiality while being suitable for practical use. Second, to support common relational data management functions, various types of queries such as exact queries, range queries, data updates, insertion and deletion should be supported. To address these issues, this paper proposes a comprehensive framework for secure and efficient query processing of relational data in the cloud. Our framework ensures data confidentiality using a salted IDA encoding scheme and column-access-via-proxy query processing primitives, and ensures query efficiency using matrix column accesses and a secure B+-tree index. In addition, our framework provides data availability and integrity. We establish the security of our proposal by a detailed security analysis and demonstrate the query efficiency of our proposal through an experimental evaluation. Cloud computing has emerged as a successful paradigm allowing individual users as well as companies to resort to external providers for storing processing data or making them available to others. Together with the many benefits, cloud computing introduces however new security and privacy risks. A major issue is that the data owner, storing data at external providers, loses control over them, leaving them potentially exposed to improper access, use, or dissemination. In this chapter, we consider the problem of protecting confidentiality of sensitive information when relying on external cloud providers for storing and processing data. We introduce confidentiality requirements and then illustrate encryption and data fragmentation as possible protection techniques. In particular, we discuss different approaches that have been proposed using encryption (with indexing) and fragmentation, either by themselves or in combination, to satisfy confidentiality requirements. Current web search engines focus on searching only themost recentsnapshot of the web. In some cases, however, it would be desirableto search over collections that include many different crawls andversions of each page. One important example of such a collectionis the Internet Archive, though there are many others. Sincethe data size of such an archive is multiple times that of a singlesnapshot, this presents us with significant performance challenges.Current engines use various techniques for index compression andoptimized query execution, but these techniques do not exploit thesignificant similarities between different versions of a page, or betweendifferent pages.In this paper, we propose a general framework for indexing andquery processing of archival collections and, more generally, anycollections with a sufficient amount of redundancy. Our approachresults in significant reductions in index size and query processingcosts on such collections, and it is orthogonal to and can be combinedwith the existing techniques. It also supports highly efficientupdates, both locally and over a network. Within this framework,we describe and evaluate different implementations that trade offindex size versus CPU cost and other factors, and discuss applicationsranging from archival web search to local search of web sites,email archives, or file systems. We present experimental resultsbased on search engine query log and a large collection consistingof multiple crawls. We present a novel peer-to-peer backup technique that allows computers connected to the Internet to back up their data cooperatively: Each computer has a set of partner computers, which collectively hold its backup data. In return, it holds a part of each partner's backup data. By adding redundancy and distributing the backup data across many partners, a highly-reliable backup can be obtained in spite of the low reliability of the average Internet machine. Because our scheme requires cooperation, it is potentially vulnerable to several novel attacks involving free riding (e.g., holding a partner's data is costly, which tempts cheating) or disruption. We defend against these attacks using a number of new methods, including the use of periodic random challenges to ensure partners continue to hold data and the use of disk-space wasting to make cheating unprofitable. Results from an initial prototype show that our technique is feasible and very inexpensive: it appears to be one to two orders of magnitude cheaper than existing Internet backup services. We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining confidentiality of the training and test data. Since the computational complexity of the homomorphic encryption scheme depends primarily on the number of levels of multiplications to be carried out on the encrypted data, we define a new class of machine learning algorithms in which the algorithm's predictions, viewed as functions of the input data, can be expressed as polynomials of bounded degree. We propose confidential algorithms for binary classification based on polynomial approximations to least-squares solutions obtained by a small number of gradient descent steps. We present experimental validation of the confidential machine learning pipeline and discuss the trade-offs regarding computational complexity, prediction accuracy and cryptographic security. To achieve reliability in distributed storage systems, data has usually been replicated across different nodes. However the increasing volume of data to be stored has motivated the introduction of erasure codes, a storage efficient alternative to replication, particularly suited for archival in data centers, where old datasets (rarely accessed) can be erasure encoded, while replicas are maintained only for the latest data. Many recent works consider the design of new storage-centric erasure codes for improved repairability. In contrast, this paper addresses the migration from replication to encoding: traditionally erasure coding is an atomic operation in that a single node with the whole object encodes and uploads all the encoded pieces. Although large datasets can be concurrently archived by distributing individual object encodings among different nodes, the network and computing capacity of individual nodes constrain the archival process due to such atomicity. We propose a new pipelined coding strategy that distributes the network and computing load of single-object encodings among different nodes, which also speeds up multiple object archival. We further present RapidRAID codes, an explicit family of pipelined erasure codes which provides fast archival without compromising either data reliability or storage overheads. Finally, we provide a real implementation of RapidRAID codes and benchmark its performance using both a cluster of 50 nodes and a set of Amazon EC2 instances. Experiments show that RapidRAID codes reduce a single object's coding time by up to 90 , while when multiple objects are encoded concurrently, the reduction is up to 20 .