Cloud storage

Summary

Cloud storage is a model of computer data storage in which the digital data is stored in logical pools, said to be on "the cloud". The physical storage spans multiple servers (sometimes in multiple locations), and the physical environment is typically owned and managed by a hosting company. These cloud storage providers are responsible for keeping the data available and accessible, and the physical environment secured, protected, and running. People and organizations buy or lease storage capacity from the providers to store user, organization, or application data. Cloud storage services may be accessed through a colocated cloud computing service, a web service application programming interface (API) or by applications that use the API, such as cloud desktop storage, a cloud storage gateway or Web-based content management systems. History Cloud computing is believed to have been invented by J. C. R. Licklider in the 1960s with his work on ARPANET to connect people and data fr

Official source

https://en.wikipedia.org/wiki/Cloud_storage

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We seek to answer the following question: To what extent can we deduplicate replicated storage ? To answer this question, we design ReDup, a secure storage system that provides users with strong integrity, reliability, and transparency guarantees about data that is outsourced at cloud storage providers. Users store multiple replicas of their data at different storage servers, and the data at each storage server is deduplicated across users. Remote data integrity mechanisms are used to check the integrity of replicas. We consider a strong adversarial model, in which collusions are allowed between storage servers and also between storage servers and dishonest users of the system. A cloud storage provider (CSP) could store less replicas than agreed upon by contract, unbeknownst to honest users. ReDup defends against such adversaries by making replica generation to be time consuming so that a dishonest CSP cannot generate replicas on the fly when challenged by the users. In addition, ReDup employs transparent deduplication, which means that users get a proof attesting the deduplication level used for their files at each replica server, and thus are able to benefit from the storage savings provided by deduplication. The proof is obtained by aggregating individual proofs from replica servers, and has a constant size regardless of the number of replica servers. Our solution scales better than state of the art and is provably secure under standard assumptions.

2018

Data storage is one of today’s fundamental services with companies, universities and research centers having the need of storing large amounts of data every day. Cloud storage services are emerging as strong alternative to local storage, allowing customers to save costs of buying and maintaining expensive hardware. Several solutions are available on the market, the most famous being Amazon S3. However it is rather difficult to access information about each service architecture, performance, and pricing. To shed light on storage services from the customer perspective, we propose a benchmarking methodology, apply it to four popular offers (Amazon S3, Amazon Glacier, Windows Azure Blob and Rackspace Cloud Files), and compare their performance. Each service is analysed as a black box and benchmarked through crafted workloads.We take the perspective of a customer located in Europe, looking for possible service providers and the optimal data center where to deploy its applications. At last, we complement the analysis by comparing the actual and forecast costs faced when using each service. According to collected results, all services show eventual weaknesses related to some workload, with no all-round eligible winner, e.g., some offers providing excellent or poor performance when exchanging large or small files. For all services, it is of paramount importance to accurately select the data center to where deploy the applications, with throughput that varies by factors from 2x to 10x. The methodology (and tools implementing it) here presented is instrumental for potential customers to identify the most suitable offer for their needs.

2014

Time in cryptography

Gwangbae Choi

Time travel has always been a fascinating topic in literature and physics. In cryptography, one may wonder how to keep data confidential for some time. In this dissertation, we will study how to make private information travel to the future. This dissertation consists of three parts: Timed-release encryption, witness encryption and self-encryption. With timed-release encryption, one can send to a counterpart a message which cannot be read before some time has elapsed. One possible solution is to use a third party. At every time period, the third party releases a time bound key, and a ciphertext which is encrypted for a time period requires the time bound key of that time period for decryption. Then, a problem occurs when the counterpart somehow loses the release time of ciphertext. We propose a solution by introducing a master time bound key which can be considered as a valid time bound key of all time periods. We propose a provably secure construction and show the experimental results. In 2018, Liu, Jager, Kakvi and Warinschi introduced a timed-release encryption scheme based on a blockchain with proof-of-work and witness encryption. Current proposals of witness encryption are based on multilinear maps. In this part, we propose a new construction without. We propose the notion of hidden group with hashing and make a witness encryption from it. We show that the construction is secure in a generic model. We propose a concrete construction based on RSA-related problems. Namely, we use an extension of the knowledge-of-exponent assumption and the order problem. We finally estimate the cost of the bitcoin blockchain implementation. Although our estimates are still high (for a release time of one hour / one year, we respectively use ciphertexts of 567 MB / 4.5 TB and a decryption time of 27 min / 5.2 months on a single core), there is room for improvement by a factor 20 000 by adapting the blockchain structure and by adopting a hash function which is better adapted to this type of programming than SHA256. In self-encryption, a device encrypts some piece of information for itself to decrypt in the future. We are interested in security of self-encryption when the state occasionally leaks. Applications where self-encryption operates are cloud storage, when a client encrypts files to be stored, and in 0-RTT session resumptions, when a server encrypts a resumption key to be stored by the client. Previous works focused on forward secrecy and resistance to replay attacks. In our work, we study post-compromise security. Post-compromise security was already solved in ratcheted instant messaging schemes, at the price of having an inflating state size. However, it was not known whether state inflation was necessary. We prove here that this is the case. In our results, we prove that post-compromise security implies a super-linear state size in terms of the number of ciphertexts which can still be decrypted by the state. We apply our result to self-encryption for cloud storage and 0-RTT session resumption, and also to secure messaging. We further show how to construct a secure scheme matching our bound on the state size.

EPFL2020

Time in cryptography

Gwangbae Choi

EPFL2020