Securely sharing and managing personal content is a challenging task in multi-device environments. In this paper, we design and implement a new platform called personal content networking (PCN). Our work is inspired by content-centric networking (CCN) because we aim to enable access to personal content using its name instead of its location. The unique challenge of PCN is to support secure file operations such as replication, updates, and access control over distributed untrusted devices. The primary contribution of this work is the design and implementation of a secure content management platform that supports secure updates, replications, and fine-grained content-centric access control of files. Furthermore, we demonstrate its feasibility through a prototype implementation on the CCNx skeleton.
Given that Attribute-Based Encryption (ABE) is designed to provide fine-grained, expressive access control, several existing works have used ABE for content sharing over untrusted storage @cite_0 @cite_7 @cite_18 . In particular, @cite_18 used a key policy ABE (KP-ABE) to provide privacy-aware content sharing over untrusted cloud storage and Proxy Re-Encryption (PRE) to delegate the task of re-encryption on cloud servers. While PCN is considered to be a cryptographic file system, unlike existing systems, PCN provides fine-grained expressive access control using CP-ABE in a fully distributed environment with untrusted nodes and it allows file owners to set up expressive access policies based on attributes (e.g. college friends, family members, etc.). Furthermore, none of the aforementioned systems provide between the name and data; thus, the channel must be secured in order to prevent man-in-the-middle attacks.
@cite_0 @cite_18 @cite_7
123899820 1993719651 2117616411 2407215383
In distributed systems users need to share sensitive objects with others based on the recipients' ability to satisfy a policy. Attribute-Based Encryption (ABE) is a new paradigm where such policies are specified and cryptographically enforced in the encryption algorithm itself. Ciphertext-Policy ABE (CP-ABE) is a form of ABE where policies are associated with encrypted data and attributes are associated with keys. In this work we focus on improving the flexibility of representing user attributes in keys. Specifically, we propose Ciphertext Policy Attribute Set Based Encryption (CP-ASBE) - a new form of CP-ABE - which, unlike existing CP-ABE schemes that represent user attributes as a monolithic set in keys, organizes user attributes into a recursive set based structure and allows users to impose dynamic constraints on how those attributes may be combined to satisfy a policy. We show that the proposed scheme is more versatile and supports many practical scenarios more naturally and efficiently. We provide a prototype implementation of our scheme and evaluate its performance overhead. With the ever increasing number of connected devices and the over abundance of data generated by these devices, data privacy has become a critical concern in the Internet of Things (IoT). One promising privacy-preservation approach is Attribute-Based Encryption (ABE), a public key encryption scheme that enables fine-grained access control, scalable key management and flexible data distribution. This paper presents an in-depth performance evaluation of ABE that focuses on execution time, data and network overhead, energy consumption, and CPU and memory usage. We evaluate two major types of ABE, Key-Policy Attribute-Based Encryption (KP-ABE) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE), on different classes of mobile devices including a laptop and a smartphone. To the best of our knowledge, this is the first comprehensive study of ABE dedicated solely to its performance. Our results provide insights into important practical issues of ABE, including what computing resources ABE requires in heterogeneous environments, at what cost ABE offers benefits, and under what situations ABE is best suited for use in the IoT. Attribute-based encryption (ABE), as introduced by Sahai and Waters, allows for fine-grained access control on encrypted data. In its key-policy flavor, the primitive enables senders to encrypt messages under a set of attributes and private keys are associated with access structures that specify which ciphertexts the key holder will be allowed to decrypt. In most ABE systems, the ciphertext size grows linearly with the number of ciphertext attributes and the only known exceptions only support restricted forms of threshold access policies. This paper proposes the first key-policy attribute-based encryption (KP-ABE) schemes allowing for non-monotonic access structures (i.e., that may contain negated attributes) and with constant ciphertext size. Towards achieving this goal, we first show that a certain class of identity-based broadcast encryption schemes generically yields monotonic KPABE systems in the selective set model. We then describe a new efficient identity-based revocation mechanism that, when combined with a particular instantiation of our general monotonic construction, gives rise to the first truly expressive KP-ABE realization with constant-size ciphertexts. The downside of these new constructions is that private keys have quadratic size in the number of attributes. On the other hand, they reduce the number of pairing evaluations to a constant, which appears to be a unique feature among expressive KP-ABE schemes. How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.