With the advent of cloud technologies, there is a growing number of easy-to-use services to store files and share them with other cloud users. By providing security features, cloud service providers try to encourage users to store personal files or corporate documents on their servers. However, their server-side encryption solutions are not satisfactory when the server itself is not trusted. Although, there are several client-side solutions to provide security for cloud sharing, they are not used extensively because of usability issues in key management. In this paper, we propose TwinCloud which is an innovative solution with the goal of providing a secure system to users without compromising the usability of cloud sharing. TwinCloud achieves this by bringing a novel solution to the complex key exchange problem and by providing a simple and practical approach to store and share files by hiding all the cryptographic and key-distribution operations from users. Serving as a gateway, TwinCloud uses two or more cloud providers to store the encryption keys and encrypted files in separate clouds which ease the secure sharing without a need for trust to either of the cloud service providers with the assumption that they do not collude with each other. We implemented TwinCloud as a lightweight application and make it available as open-source. The results of our usability study show the prospect of the secure sharing solution of TwinCloud.
This section examines preceding academic and industrial work related to secure file sharing on clouds. @cite_7 proposed a trusted data sharing method over untrusted cloud storage providers. This method can be described as follows:
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Cloud computing has been acknowledged as one of the prevaling models for providing IT capacities. The off-premises computing paradigm that comes with cloud computing has incurred great concerns on the security of data, especially the integrity and confidentiality of data, as cloud service providers may have complete control on the computing infrastructure that underpins the services. This makes it difficult to share data via cloud providers where data should be confidential to the providers and only authorized users should be allowed to access the data. This work aims to construct a system for trusted data sharing through untrusted cloud providers, to address the above mentioned issue. The constructed system can imperatively impose the access control policies of data owners, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data. Modern cloud services are designed to encourage and support collaboration. To help users share links to online documents, maps, etc., several services, including cloud storage providers such as Microsoft OneDrive and mapping services such as Google Maps, directly integrate URL shorteners that convert long, unwieldy URLs into short URLs, consisting of a domain such as 1drv.ms or goo.gl and a short token. In this paper, we demonstrate that the space of 5- and 6-character tokens included in short URLs is so small that it can be scanned using brute-force search. Therefore, all online resources that were intended to be shared with a few trusted friends or collaborators are effectively public and can be accessed by anyone. This leads to serious security and privacy vulnerabilities. In the case of cloud storage, we focus on Microsoft OneDrive. We show how to use short-URL enumeration to discover and read shared content stored in the OneDrive cloud, including even files for which the user did not generate a short URL. 7 of the OneDrive accounts exposed in this fashion allow anyone to write into them. Since cloud-stored files are automatically copied into users' personal computers and devices, this is a vector for large-scale, automated malware injection. In the case of online maps, we show how short-URL enumeration reveals the directions that users shared with each other. For many individual users, this enables inference of their residential addresses, true identities, and extremely sensitive locations they visited that, if publicly revealed, would violate medical and financial privacy. Cloud computing is a new computing paradigm that allows users with different computing demands to access a shared pool of configurable computing resources (e.g., servers, network, storage, database, applications and services). Many commercial cloud providers have emerged in the past 6-7 years, and each typically provides its own cloud infrastructure, APIs and application description formats to access the cloud resources, as well as support for service level agreements (SLAs). Such vendor lock-in has seriously limited the flexibility that cloud end users would like to process, when it comes to deploy applications over different infrastructures in different geographic locations, or to migrate a service from one provider's cloud to another. To enable seamless sharing of resources from a pool of cloud providers, efforts have emerged recently to facilitate cloud interoperability, i.e., the ability for multiple cloud providers to work together, from both the industry and academia. In this article, we conduct a comprehensive survey on the state-of-the-art efforts, with a focus on interoperability among different IaaS (infrastructure as a service) cloud platforms. We investigate the existing studies on taxonomies and standardization of cloud interoperability, as well as practical cloud technologies from both the cloud provider's and user's perspectives to enable interoperation. We pose issues and challenges to advance the topic area, and hope to pave a way for the forthcoming research. Trust and security have prevented businesses from fully accepting cloud platforms. To protect clouds, providers must first secure virtualized data center resources, uphold user privacy, and preserve data integrity. The authors suggest using a trust-overlay network over multiple data centers to implement a reputation system for establishing trust between service providers and data owners. Data coloring and software watermarking techniques protect shared data objects and massively distributed software modules. These techniques safeguard multi-way authentications, enable single sign-on in the cloud, and tighten access control for sensitive data in both public and private clouds. Induced by incorporating the powerful data storage and data processing abilities of cloud computing (CC) as well as ubiquitous data gathering capability of wireless sensor networks (WSNs), CC-WSN integration received a lot of attention from both academia and industry. However, authentication as well as trust and reputation calculation and management of cloud service providers (CSPs) and sensor network providers (SNPs) are two very critical and barely explored issues for this new paradigm. To fill the gap, this paper proposes a novel authenticated trust and reputation calculation and management (ATRCM) system for CC-WSN integration. Considering the authenticity of CSP and SNP, the attribute requirement of cloud service user (CSU) and CSP, the cost, trust, and reputation of the service of CSP and SNP, the proposed ATRCM system achieves the following three functions: 1) authenticating CSP and SNP to avoid malicious impersonation attacks; 2) calculating and managing trust and reputation regarding the service of CSP and SNP; and 3) helping CSU choose desirable CSP and assisting CSP in selecting appropriate SNP. Detailed analysis and design as well as further functionality evaluation results are presented to demonstrate the effectiveness of ATRCM, followed with system security analysis. Searchable symmetric encryption (SSE) allows a client to encrypt its data in such a way that this data can still be searched. The most immediate application of SSE is to cloud storage, where it enables a client to securely outsource its data to an untrusted cloud provider without sacrificing the ability to search over it. SSE has been the focus of active research and a multitude of schemes that achieve various levels of security and efficiency have been proposed. Any practical SSE scheme, however, should (at a minimum) satisfy the following properties: sublinear search time, security against adaptive chosen-keyword attacks, compact indexes and the ability to add and delete files efficiently. Unfortunately, none of the previously-known SSE constructions achieve all these properties at the same time. This severely limits the practical value of SSE and decreases its chance of deployment in real-world cloud storage systems. To address this, we propose the first SSE scheme to satisfy all the properties outlined above. Our construction extends the inverted index approach (, CCS 2006) in several non-trivial ways and introduces new techniques for the design of SSE. In addition, we implement our scheme and conduct a performance evaluation, showing that our approach is highly efficient and ready for deployment. Cloud Computing is a recent paradigm that is creating high expectations about benefits such as the pay-per-use model and elasticity of resources. However, with this optimism come also concerns about security. In a public cloud, the user's data storage and processing is no longer done inside its premises, but in data centers owned and administrated by the cloud provider. This may be a concern for organizations that deal with critical data, such as medical records. We show that a malicious insider can steal confidential data of the cloud user, so the user is mostly left with trusting the cloud provider. The paper achieves this goal by showing a set of attacks that demonstrate how a malicious insider can easily obtain passwords, cryptographic keys, files and other confidential data. Additionally, the paper shows that recent research results that might be useful to protect data in the cloud, are still not enough to deal with the problem. The paper is a call to arms for research in the topic. Cloud storage services enable users to remotely access data in a cloud anytime and anywhere, using any device, in a pay-as-you-go manner. Moving data into a cloud offers great convenience to users since they do not have to care about the large capital investment in both the deployment and management of the hardware infrastructures. However, allowing a cloud service provider (CSP), whose purpose is mainly for making a profit, to take the custody of sensitive data, raises underlying security and privacy issues. To keep user data confidential against an untrusted CSP, a natural way is to apply cryptographic approaches, by disclosing the data decryption key only to authorized users. However, when a user wants to retrieve files containing certain keywords using a thin client, the adopted encryption system should not only support keyword searching over encrypted data, but also provide high performance. In this paper, we investigate the characteristics of cloud storage services and propose a secure and privacy preserving keyword searching (SPKS) scheme, which allows the CSP to participate in the decipherment, and to return only files containing certain keywords specified by the users, so as to reduce both the computational and communication overhead in decryption for users, on the condition of preserving user data privacy and user querying privacy. Performance analysis shows that the SPKS scheme is applicable to a cloud environment. Cloud computing, as an emerging computing paradigm, enables users to remotely store their data into a cloud so as to enjoy scalable services on-demand. Especially for small and medium-sized enterprises with limited budgets, they can achieve cost savings and productivity enhancements by using cloud-based services to manage projects, to make collaborations, and the like. However, allowing cloud service providers (CSPs), which are not in the same trusted domains as enterprise users, to take care of confidential data, may raise potential security and privacy issues. To keep the sensitive user data confidential against untrusted CSPs, a natural way is to apply cryptographic approaches, by disclosing decryption keys only to authorized users. However, when enterprise users outsource confidential data for sharing on cloud servers, the adopted encryption system should not only support fine-grained access control, but also provide high performance, full delegation, and scalability, so as to best serve the needs of accessing data anytime and anywhere, delegating within enterprises, and achieving a dynamic set of users. In this paper, we propose a scheme to help enterprises to efficiently share confidential data on cloud servers. We achieve this goal by first combining the hierarchical identity-based encryption (HIBE) system and the ciphertext-policy attribute-based encryption (CP-ABE) system, and then making a performance-expressivity tradeoff, finally applying proxy re-encryption and lazy re-encryption to our scheme. Plutus is a cryptographic storage system that enables secure file sharing without placing much trust on the file servers. In particular, it makes novel use of cryptographic primitives to protect and share files. Plutus features highly scalable key management while allowing individual users to retain direct control over who gets access to their files. We explain the mechanisms in Plutus to reduce the number of cryptographic keys exchanged between users by using filegroups, distinguish file read and write access, handle user revocation efficiently, and allow an untrusted server to authorize file writes. We have built a prototype of Plutus on OpenAFS. Measurements of this prototype show that Plutus achieves strong security with overhead comparable to systems that encrypt all network traffic. Researchers have been studying security challenges of database outsourcing for almost a decade. Privacy of outsourced data is one of the main challenges when the “Database As a Service” model is adopted in the service oriented trend of the cloud computing paradigm. This is due to the insecurity of the network environment or even the untrustworthiness of the service providers. This paper proposes a method to preserve privacy of outsourced data based on Shamir’s secret sharing scheme. We split attribute values into several parts and distribute them among untrusted servers. The problem of using secret sharing in data outsourcing scenario is how to search efficiently within the randomly generated pool of shares. In this paper, at first, we customize Shamir’s scheme to have A Searchable Secret Sharing Scheme (AS4) that enables the efficient execution of different kinds of queries over distributed shares. Then, we extend our method for sharing values to A Secure Searchable Secret Sharing Scheme (AS5) to tolerate statistical attacks based on adversary’s knowledge about outsourced data distribution. In AS5 data shares are generated uniformly across a domain to prevent information leakage about the outsourced data. Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with data ownership. Our design is tailored for IoT data streams and enables secure data sharing. We enable a secure and resilient access control management, by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. We facilitate the storage of time-series IoT data at the edge of the network via a locality-aware decentralized storage system that is managed with the blockchain technology. Our system is agnostic of the physical storage nodes and supports as well utilization of cloud storage resources as storage nodes. Cloud computing is set of resources and services offered through the Internet. Cloud services are delivered from data centres located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. The rapid growth in field of "cloud computing" also increases severe security concerns. Security has remained a constant issue for Open Systems and internet, when we are talking about security, cloud really suffers. Lack of security is the only hurdle in wide adoption of cloud computing. Cloud computing is surrounded by many security issues like securing data and examining the utilization of cloud by the cloud computing vendors. This paper proposes a scheme to securely store and access of data via internet. We have used ECC based PKI for certificate procedure because the use of ECC significantly reduces the computation cost, message size and transmission overhead over RSA based PKI as 160-bit key size in ECC provides comparable security with 1024-bit key in RSA. We have designed Secured Cloud Storage Framework (SCSF). In this framework, users not only can securely store and access data in cloud but also can share data with multiple users through the unsecure internet in a secured way. This scheme can ensure the security and privacy of the data in the cloud. Users are increasingly storing, accessing, and exchanging data through public cloud services such as those provided by Google, Facebook, Apple, and Microsoft. Although users may want to have faith in cloud providers to provide good security protection, the confidentiality of any data in public clouds can be violated, and consequently, while providers may not be "doing evil," we can not and should not trust them with data confidentiality. To better protect the privacy of user data stored in the cloud, in this paper we propose a privacy-preserving system called Mimesis Aegis (M-Aegis) that is suitable for mobile platforms. M-Aegis is a new approach to user data privacy that not only provides isolation but also preserves the user experience through the creation of a conceptual layer called Layer 7.5 (L-7.5), which is interposed between the application (OSI Layer 7) and the user (Layer 8). This approach allows M-Aegis to implement true end-to-end encryption of user data with three goals in mind: 1) complete data and logic isolation from untrusted entities; 2) the preservation of original user experience with target apps; and 3) applicable to a large number of apps and resilient to app updates. In order to preserve the exact application workflow and look-and-feel, M-Aegis uses L-7.5 to put a transparent window on top of existing application GUIs to both intercept plaintext user input before transforming the input and feeding it to the underlying app, and to reverse-transform the output data from the app before displaying the plaintext data to the user. This technique allows M-Aegis to transparently integrate with most cloud services without hindering usability and without the need for reverse engineering. We implemented a prototype of M-Aegis on Android and show that it can support a number of popular cloud services, e.g. Gmail, Facebook Messenger, WhatsApp, etc. Our performance evaluation and user study show that users incur minimal overhead when adopting M-Aegis on Android: imperceptible encryption decryption latency and a low and adjustable false positive rate when searching over encrypted data. In recent years, the paradigm of cloud computing has gained an increasing interest from the academic community as well as from the commercial point of view. The cloud is a very appealing concept both for the providers (who can benefit from hiring out their extra computation and storage resources) and for the users (who can avoid the initial investment on resources by outsourcing their processes and data to a cloud). Searchable (symmetric) encryption allows encryption while still enabling search for keywords. Its immediate application is cloud storage where a client outsources its files while the (cloud) service provider should search and selectively retrieve those. Searchable encryption is an active area of research and a number of schemes with different efficiency and security characteristics have been proposed in the literature. Any scheme for practical adoption should be efficient -- i.e. have sub-linear search time --, dynamic -- i.e. allow updates -- and semantically secure to the most possible extent. Unfortunately, efficient, dynamic searchable encryption schemes suffer from various drawbacks. Either they deteriorate from semantic security to the security of deterministic encryption under updates, they require to store information on the client and for deleted files and keywords or they have very large index sizes. All of this is a problem, since we can expect the majority of data to be later added or changed. Since these schemes are also less efficient than deterministic encryption, they are currently an unfavorable choice for encryption in the cloud. In this paper we present the first searchable encryption scheme whose updates leak no more information than the access pattern, that still has asymptotically optimal search time, linear, very small and asymptotically optimal index size and can be implemented without storage on the client (except the key). Our construction is based on the novel idea of learning the index for efficient access from the access pattern itself. Furthermore, we implement our system and show that it is highly efficient for cloud storage. Cloud computing provides users near instant access to seemingly unlimited resources, and provides service providers the opportunity to deploy complex information technology infrastructure, as a service, to their customers. Providers bene t from economies of scale and multiplexing gains a orded by sharing of resources through virtualization of the underlying physical infrastructure. However, the scale and highly dynamic nature of cloud platforms impose signi cant new challenges to cloud service providers. In particular, realizing sophisticated cloud services requires a cloud control framework that can orchestrate cloud resource provisioning, con guration, utilization and decommissioning across a distributed set of physical resources. In this paper we advocate a data-centric approach to cloud orchestration. Following this approach, cloud resources are modeled as structured data that can be queried by a declarative language, and updated with well-de ned transactional semantics. We examine the feasibility, bene ts and challenges of the approach, and present our design and prototype implementation of the Data-centric Management Framework (DMF) as a solution, with data models, query languages and semantics that are speci cally designed for cloud resource orchestration. Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access information that is relevant to their present location. Users are interested in querying about points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping such information up-to-date and relevant to the users is not an easy task, so the owners of such data sets will make the data accessible only to paying customers. Users send their current location as the query parameter, and wish to receive as result the nearest POIs, i.e., nearest-neighbors (NNs). But typical data owners do not have the technical means to support processing queries on a large scale, so they outsource data storage and querying to a cloud service provider. Many such cloud providers exist who offer powerful storage and computational infrastructures at low cost. However, cloud providers are not fully trusted, and typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to answer queries correctly, but they also collect the locations of the POIs and the subscribers for other purposes. Leakage of POI locations can lead to privacy breaches as well as financial losses to the data owners, for whom the POI data set is an important source of revenue. Disclosure of user locations leads to privacy violations and may deter subscribers from using the service altogether. In this paper, we propose a family of techniques that allow processing of NN queries in an untrusted outsourced environment, while at the same time protecting both the POI and querying users’ positions. Our techniques rely on mutable order preserving encoding (mOPE), the only secure order-preserving encryption method known to-date. We also provide performance optimizations to decrease the computational cost inherent to processing on encrypted data, and we consider the case of incrementally updating data sets. We present an extensive performance evaluation of our techniques to illustrate their viability in practice. Aiming to reduce the cost and complexity of maintaining networking infrastructures, organizations are increasingly outsourcing their network functions (e.g., firewalls, traffic shapers and intrusion detection systems) to the cloud, and a number of industrial players have started to offer network function virtualization (NFV)-based solutions. Alas, outsourcing network functions in its current setting implies that sensitive network policies, such as firewall rules, are revealed to the cloud provider. In this paper, we investigate the use of cryptographic primitives for processing outsourced network functions, so that the provider does not learn any sensitive information. More specifically, we present a cryptographic treatment of privacy-preserving outsourcing of network functions, introducing security definitions as well as an abstract model of generic network functions, and then propose a few instantiations using partial homomorphic encryption and public-key encryption with keyword search. We include a proof-of-concept implementation of our constructions and show that network functions can be privately processed by an untrusted cloud provider in a few milliseconds. Cloud storage is becoming increasingly popular among individuals and businesses. Amazon Cloud Drive is a flavor of cloud-based storage that allows users to transfer files to and from multiple computers, with or without the use of a separate application that must be installed on the user's machine. This paper discusses the digital artifacts left behind after an Amazon Cloud Drive has been accessed or manipulated from a computer. Methods available to a forensic examiner that can be used to determine file transfers that occurred to and from an Amazon Cloud Drive on a computer, as well as retrieving relevant Cloud Drive artifacts from unallocated space is discussed in this paper. Two Perl scripts are also introduced to help automate the process of retrieving information from Amazon Cloud Drive artifacts.