CP-ABE Access Control Scheme for Sensitive Data Set Constraint with Hidden Access Policy and Constraint Policy

CP-ABE (Ciphertext-Policy Attribute-Based Encryption) with hidden access control policy enables data owners to share their encrypted data using cloud storage with authorized users while keeping the access control policies blinded. However, a mechanism to prevent users from achieving successive access to a data owner’s certain number of data objects, which present a conflict of interest or whose combination thereof is sensitive, has yet to be studied. In this paper, we analyze the underlying relations among these particular data objects, introduce the concept of the sensitive data set constraint, and propose a CP-ABE access control scheme with hidden attributes for the sensitive data set constraint. This scheme incorporates extensible, partially hidden constraint policy. In our scheme, due to the separation of duty principle, the duties of enforcing the access control policy and the constraint policy are divided into two independent entities to enhance security. The hidden constraint policy provides flexibility in that the data owner can partially change the sensitive data set constraint structure after the system has been set up

Attribute-based encryption for cloud computing access control: A survey

National Research Foundation (NRF) Singapore; AXA Research Fun

AnonyControl: Control Cloud Data Anonymously with Multi-Authority Attribute-Based Encryption

Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. However, those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute- Based Encryption (ABE) have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address the user and data privacy problem in a cloud. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access, finegrained privilege control, and more importantly, tolerance to up to (N -2) authority compromise. Our security and performance analysis show that AnonyControl is both secure and efficient for cloud computing environment.Comment: 9 pages, 6 figures, 3 tables, conference, IEEE INFOCOM 201

A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage

Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table

Data exploitation and privacy protection in the era of data sharing

As the amount, complexity, and value of data available in both private and public sectors has risen sharply, the competing goals of data privacy and data utility have challenged both organizations and individuals. This dissertation addresses both goals. First, we consider the task of {\it interorganizational data sharing}, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. A key challenge in this type of scenario is that each organization uses its own set of proprietary, intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparable with the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic and efficient interorganizational data sharing, while allowing each party to use its own set of proprietary attributes to describe the shared data and preserving confidentiality of both data records and attributes. We introduce the novel technique of \textit{attribute-based encryption with oblivious attribute translation (OTABE)}, which plays a crucial role in our solution and may prove useful in other applications. This extension of attribute-based encryption uses semi-trusted proxies to enable dynamic and oblivious translation between proprietary attributes that belong to different organizations. We prove that our OTABE-based framework is secure in the standard model and provide two real-world use cases. Next, we turn our attention to utility that can be derived from the vast and growing amount of data about individuals that is available on social media. As social networks (SNs) continue to grow in popularity, it is essential to understand what can be learned about personal attributes of SN users by mining SN data. The first SN-mining problem we consider is how best to predict the voting behavior of SN users. Prior work only considered users who generate politically oriented content or voluntarily disclose their political preferences online. We avoid this bias by using a novel type of Bayesian-network (BN) model that combines demographic, behavioral, and social features. We test our method in a predictive analysis of the 2016 U.S. Presidential election. Our work is the first to take a semi-supervised approach in this setting. Using the Expectation-Maximization (EM) algorithm, we combine labeled survey data with unlabeled Facebook data, thus obtaining larger datasets and addressing self-selection bias. The second SN-mining challenge we address is the extent to which Dynamic Bayesian Networks (DBNs) can infer dynamic behavioral intentions such as the intention to get a vaccine or to apply for a loan. Knowledge of such intentions has great potential to improve the design of recommendation systems, ad-targeting mechanisms, public-health campaigns, and other social and commercial endeavors. We focus on the question of how to infer an SN user\u27s \textit{offline} decisions and intentions using only the {\it public} portions of her \textit{online} SN accounts. Our contribution is twofold. First, we use BNs and several behavioral-psychology techniques to model decision making as a complex process that both influences and is influenced by static factors (such as personality traits and demographic categories) and dynamic factors (such as triggering events, interests, and emotions). Second, we explore the extent to which temporal models may assist in the inference task by representing SN users as sets of DBNs that are built using our modeling techniques. The use of DBNs, together with data gathered in multiple waves, has the potential to improve both inference accuracy and prediction accuracy in future time slots. It may also shed light on the extent to which different factors influence the decision-making process

CUPS : Secure Opportunistic Cloud of Things Framework based on Attribute Based Encryption Scheme Supporting Access Policy Update

The ever‐growing number of internet connected devices, coupled with the new computing trends, namely within emerging opportunistic networks, engenders several security concerns. Most of the exchanged data between the internet of things (IoT) devices are not adequately secured due to resource constraints on IoT devices. Attribute‐based encryption is a promising cryptographic mechanism suitable for distributed environments, providing flexible access control to encrypted data contents. However, it imposes high decryption costs, and does not support access policy update, for highly dynamic environments. This paper presents CUPS, an ABE‐based framework for opportunistic cloud of things applications, that securely outsources data decryption process to edge nodes in order to reduce the computation overhead on the user side. CUPS allows end‐users to offload most of the decryption overhead to an edge node and verify the correctness of the received partially decrypted data from the edge node. Moreover, CUPS provides the access policy update feature with neither involving a proxy‐server, nor re‐encrypting the enciphered data contents and re‐distributing the users' secret keys. The access policy update feature in CUPS does not affect the size of the message received by the end‐user, which reduces the bandwidth and the storage usage. Our comprehensive theoretical analysis proves that CUPS outperforms existing schemes in terms of functionality, communication and computation overheads

ESPOONERBAC_{{ERBAC}}: Enforcing Security Policies In Outsourced Environments

Data outsourcing is a growing business model offering services to individuals and enterprises for processing and storing a huge amount of data. It is not only economical but also promises higher availability, scalability, and more effective quality of service than in-house solutions. Despite all its benefits, data outsourcing raises serious security concerns for preserving data confidentiality. There are solutions for preserving confidentiality of data while supporting search on the data stored in outsourced environments. However, such solutions do not support access policies to regulate access to a particular subset of the stored data. For complex user management, large enterprises employ Role-Based Access Controls (RBAC) models for making access decisions based on the role in which a user is active in. However, RBAC models cannot be deployed in outsourced environments as they rely on trusted infrastructure in order to regulate access to the data. The deployment of RBAC models may reveal private information about sensitive data they aim to protect. In this paper, we aim at filling this gap by proposing \textbf{$\mathit{ESPOON_{ERBAC}}$} for enforcing RBAC policies in outsourced environments. $\mathit{ESPOON_{ERBAC}}$ enforces RBAC policies in an encrypted manner where a curious service provider may learn a very limited information about RBAC policies. We have implemented $\mathit{ESPOON_{ERBAC}}$ and provided its performance evaluation showing a limited overhead, thus confirming viability of our approach.Comment: The final version of this paper has been accepted for publication in Elsevier Computers & Security 2013. arXiv admin note: text overlap with arXiv:1306.482