With the increased use of Internet, governments and large companies store and share massive amounts of personal data in such a way that leaves no space for transparency. When a user needs to achieve a simple task like applying for college or a driving license, he needs to visit a lot of institutions and organizations, thus leaving a lot of private data in many places. The same happens when using the Internet. These privacy issues raised by the centralized architectures along with the recent developments in the area of serverless applications demand a decentralized private data layer under user control.
Note that @cite_9 introduces the data sovereignty notion for establishing the nation-state where the cloud storage service providers are storing the data physically in order to ensure they are meeting their contractual geographic obligations. In this paper, we consider data sovereignty to be the ability of the user to have full control over his data and the entities to which it is shared or revoked.
@cite_9
1550039286 1993369312 2102162364 2169270531
In this paper we define the problem and scope of data sovereignty - the coupling of stored data authenticity and geographical location in the cloud. Establishing sovereignty is an especially important concern amid legal and policy constraints when data and resources are virtualized and widely distributed. We identify the key challenges that need to be solved to achieve an effective and un-cheatable solution as well as propose an initial technique for data sovereignty. Cloud computing services enable organizations and individuals to outsource the management of their data to a service provider in order to save on hardware investments and reduce maintenance costs. Only authorized users are allowed to access the data. Nobody else, including the service provider, should be able to view the data. For instance, a real-estate company that owns a large database of properties wants to allow its paying customers to query for houses according to location. On the other hand, the untrusted service provider should not be able to learn the property locations and, e.g., selling the information to a competitor. To tackle the problem, we propose to transform the location datasets before uploading them to the service provider. The paper develops a spatial transformation that re-distributes the locations in space, and it also proposes a cryptographic-based transformation. The data owner selects the transformation key and shares it with authorized users. Without the key, it is infeasible to reconstruct the original data points from the transformed points. The proposed transformations present distinct trade-offs between query efficiency and data confidentiality. In addition, we describe attack models for studying the security properties of the transformations. Empirical studies demonstrate that the proposed methods are efficient and applicable in practice. 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. People increasingly generate content on their mobile devices and upload it to third-party services such as Facebook and Google Latitude for sharing and backup purposes. Although these services are convenient and useful, their use has important privacy implications due to their centralized nature and their acquisitions of rights to user-contributed content. This paper argues that people's interests would be be better served by uploading their data to a machine that they themselves own and control. We term these machines Virtual Individual Servers (VISs) because our preferred instantiation is a virtual machine running in a highly-available utility computing infrastructure. By using VISs, people can better protect their privacy because they retain ownership of their data and remain in control over the software and policies that determine what data is shared with whom. This paper also describes a range of applications of VIS proxies. It then presents our initial implementation and evaluation of one of these applications, a decentralized framework for mobile social services based on VISs. Our experience so far suggests that building such applications on top of the VIS concept is feasible and desirable.