ANEXOS DEL PLIEGO

We first discuss systems that leverage untrusted remote storage services to provide data storage with data integrity guarantees (in Section 2.2.1). In Section 2.2.2, we describe existing systems aimed at personal data storage across user devices. Lastly, in Section2.2.3 we discuss other storage systems such as OpenTSDB [27] (which we use in Chapter 4as a point of comparison).

2.2.1 Leveraging Untrusted Remote Storage

Li et al. [128] build SUNDR, a network file system that uses untrusted remote storage services to store files and provides integrity and consistency guarantees for stored files.

It employs cryptographic mechanisms such as digital signatures and fork consistency to protect file system contents and enables clients to detect any unauthorized modifications.

The authors show that a malicious user with complete administrative control of a SUNDR

server cannot cause the clients to read altered contents of stored files. Using example workloads such as the Concurrent Versions System (CVS), they demonstrate that the latency overhead of SUNDR is minimal as compared to NFS [157].

Extending the approach in SUNDR [128], SPORC [82] provides a “generic collaboration service” using a cloud-hosted server running on a VM. The service is used to instantiate different applications such as a key-value store and a collaborative text editor. The design of the service ensures that the cloud-hosted server only receives encrypted data. It uses

“fork* consistency” and “operational transformation” mechanisms to enable clients to de-tect unauthorized operations such as additions, modifications, deletions, or re-orderings.

The authors use the example workloads to demonstrate that client latency overheads are negligible.

Venus [168] provides key-value storage using commodity cloud storage services such as Amazon S3 [4]. It does not require users to rent cloud based virtual machines for server hosting (as in SPORC [82]). More importantly, it does not require users to trust storage service providers, provides data integrity and consistency guarantees, and detects any data tampering by a service provider. Similar to Venus, Depot [134] provides a key-value store which although using untrusted storage services, provides consistency, staleness, durability, and recovery properties.

Farsite [45] proposes using a collection of insecure and unreliable desktop computers to instantiate a reliable virtual file server with limited data integrity guarantees. Chefs [87]

demonstrates content distribution by replication of an entire file system on untrusted stor-age servers.

Although this body of work demonstrates the use of different techniques to leverage untrusted storage services (including commodity cloud storage services), it does not address efficient storage and low-latency retrieval of time-series data.

2.2.2 Storing Data Across Devices

Salmon et al. [156] propose Perspective, a semantic file system to help users in manag-ing their data spread across their personal devices, such as laptops and smartphones. It provides a uniform abstraction called a view, which is a semantic description of a set of files. It is specified as a query on file attributes and the IDs of devices on which the files are stored. Multiple devices interface with each other in a peer-to-peer (P2P) fashion, to provide the unified view abstraction.

Similar to Perspective [156], HomeViews [89] provides users with a view abstraction, and also allows secure sharing of views across user devices. It works in a P2P fashion and

does not require users to manage a centralized account or data protection mechanisms.

Both Perspective [156] and HomeViews [89] target user data such as photos, music, and documents, stored across user devices.

Goh et al. [91] propose SiRiUS, a system that supplements local device file system stor-age with untrusted remote storstor-age. It forms an overlay over local file systems and other network and P2P file systems such as NFS [157] and OceanStore. It provides some data in-tegrity and confidentiality guarantees, however, it does not guarantee the freshness of data a reader receives. Similarly, VStore++ [111,112] provides a “virtual store” abstraction, which is dynamically mapped to suitable user devices or cloud storage.

This body of work addresses many challenges associated with managing data produced and consumed by applications across user devices. However, it does not specifically address storage and retrieval of time-series data. Moreover, it does not focus on data confidentiality and integrity guarantees for such data.

2.2.3 Other Systems

sMAP [74] explores the design of a RESTful service that mediates data transfers be-tween sensors (the data sources), and data consumers such as applications. It provides interpretability of data streams, manages the consolidation and propagation of data from sensors to a given consumer, and focuses on supporting a wide variety of sensors. However it does not focus on efficient storage and low-latency retrieval of sensor data.

Stream processing systems also present alternative solutions for housing sensor data.

They enable data to be pushed through a data-processing subsystem, offer straight-through processing, that is, no modification to incoming data messages, which are then stored.

Examples of such systems include Aurora [62] and Borealis [43]. However, these system assume that entities such as the data writer, data reader, and storage servers, are in a single administrative domain. Scenarios where users want to leverage commodity storage services such as Amazon S3 [4] or Windows Azure [37] are therefore not supported. Specialized time-series databases such as OpenTSDB [27] (described next) also suffer from this drawback.

OpenTSDB [27] is a popular time-series data storage system. It builds on top of HBase [6] (a column oriented database management system), and exposes a data stream abstraction for reading and writing sensor data. Applications using OpenTSDB can store and retrieve data streams by using its HTTP-based web APIs. However, OpenTSDB does not provide any data integrity guarantees. This leaves data vulnerable to modifications by malicious third parties such as a service provider hosting OpenTSDB instances.

Ming et al. [129] propose a framework for storage and retrieval of patient health records (PHR). The framework divides users into subsets, and provides each subset with a separate set of cryptographic keys for encrypting data before uploading to a cloud server. Using attribute based encryption techniques, the authors demonstrate reduction in key distribu-tion complexity with support for revocadistribu-tion of user access rights. However, this work relies on cooperation from the cloud hosted server (while assuming it to be non-malicious), and does not focus on time-series data.

Popa et al. [146] design a cloud storage system which provides users with data integrity and freshness guarantees. However, they do not address storage of time-series data, and do not focus on leveraging existing commodity cloud storage services.