In today's ultra-connected world, the production and consumption of digital data has become immensely huge in volume. Differential privacy is a relatively new approach which attempts to provide strong privacy protection to users' data, while still maintaining outside access to data. However, the comparison of these methods in terms of performance and database suitability remains an open question. In this paper, we will provide a comprehensive comparison of input and output data perturbations for differentially private databases and evaluate the results in terms of accuracy, privacy, efficiency and scalability.

Data generated and stored worldwide is increasing multifold every year, with images and media content accounting for a large portion of this data. In addition to volume, ensuring adequate security is an important challenge that needs to be addressed. In this paper, we propose an efficient, secure data-storage approach based on compressive sensing. Our approach uses data deduplication to remove identical copies of data. Our experimental results show significant storage savings, while providing strong level security.

With the advent of cloud computing as a new paradigm and technology, and the increased tendency of decision makers to envision a staged migration to cloud services, most enterprises are choosing to outsource their data to cloud storage providers, for better management of their IT resources, in terms of security, control, space and storage costs. In this context, assuming that the cloud service provider may not be trustworthy (i.e. is honest but curious), ensuring data privacy in all operations performed on enterprise data while these data reside in the Cloud is still a challenge. This paper proposes a novel twolevel data deduplication framework that can be used in cloud storage by enterprises. At the enterprise level, the enterprise performs cross-user data deduplication and outsources its data to the Cloud. At the cloud storage provider level, cross enterprise data deduplication is performed by the cloud service provider to further remove duplicates, resulting in cost and space savings. We argue that our framework will allow the enterprise to facilitate operations such as searching over encrypted data, sharing data within the enterprise, and downloading data from the Cloud directly, in a secure and efficient manner without the need to trust the cloud service provider.

Cloud computing has empowered the individual user by providing seemingly unlimited storage space and availability and accessibility of data anytime and anywhere. Cloud service providers are able to maximize data storage space by incorporating data deduplication into cloud storage. Although data deduplication removes data redundancy and data replication, it also introduces major data privacy and security issues for the user. In this paper, a new privacy-preserving framework that addresses this issue is proposed. Our framework uses an efficient deduplication algorithm to divide a given file into smaller units. These units are then encrypted by the user using the combination of a secure hash function and a block encryption algorithm. An index tree of hash values of these units is also generated and encrypted using an asymmetric search encryption scheme by the user. This index tree will enable the cloud service provider to search through the index and return the requested units. We will show that our proposed framework will allow cloud service and storage providers to employ data deduplication techniques without giving them access to either the users' plaintexts or the users' decryption keys.