Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF $\;\times\;$ IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.

A Survey on Big Data Market: Pricing, Trading and Protection

With the advent of cloud computing, it has become increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve this data. For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. In this paper, we propose schemes to deal with privacy preserving ranked multi-keyword search in a multi-owner model (PRMSM). To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel additive order and privacy preserving function family. To prevent the attackers from eavesdropping secret keys and pretending to be legal data users submitting searches, we propose a novel dynamic secret key generation protocol and a new data user authentication protocol. Furthermore, PRMSM supports efficient data user revocation. Extensive experiments on real-world datasets confirm the efficacy and efficiency of PRMSM.

Privacy Preserving Ranked Multi-Keyword Search for Multiple Data Owners in Cloud Computing

Verifiable Searchable Symmetric Encryption, as an important cloud security technique, allows users to retrieve the encrypted data from the cloud through keywords and verify the validity of the returned results. Dynamic update for cloud data is one of the most common and fundamental requirements for data owners in such schemes. To the best of our knowledge, the existing verifiable SSE schemes supporting data dynamic update are all based on asymmetric-key cryptography verification, which involves time-consuming operations. The overhead of verification may become a significant burden due to the sheer amount of cloud data. Therefore, how to achieve keyword search over dynamic encrypted cloud data with efficient verification is a critical unsolved problem. To address this problem, we explore achieving keyword search over dynamic encrypted cloud data with symmetric-key based verification and propose a practical scheme in this paper. In order to support the efficient verification of dynamic data, we design a novel Accumulative Authentication Tag ( AAT ) based on the symmetric-key cryptography to generate an authentication tag for each keyword. Benefiting from the accumulation property of our designed AAT , the authentication tag can be conveniently updated when dynamic operations on cloud data occur. In order to achieve efficient data update, we design a new secure index composed by a search table ST based on the orthogonal list and a verification list VL containing AATs. Owing to the connectivity and the flexibility of ST, the update efficiency can be significantly improved. The security analysis and the performance evaluation results show that the proposed scheme is secure and efficient.

Towards Achieving Keyword Search over Dynamic Encrypted Cloud Data with Symmetric-Key Based Verification

With fast-growing technology, online social networks (OSNs) have exploded in popularity over the past few years. The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues. The information shared in social network and media spreads very fast, almost instantaneously which makes it attractive for attackers to gain information. Secrecy and surety of OSNs need to be inquired from various positions. There are numerous security and privacy issues related to the user’s shared information especially when a user uploads personal content such as photos, videos, and audios. The attacker can maliciously use shared information for illegitimate purposes. The risks are even higher if children are targeted. To address these issues, this paper presents a thorough review of different security and privacy threats and existing solutions that can provide security to social network users. We have also discussed OSN attacks on various OSN web applications by citing some statistics reports. In addition to this, we have discussed numerous defensive approaches to OSN security. Finally, this survey discusses open issues, challenges, and relevant security guidelines to achieve trustworthiness in online social networks.

/pdf/online-social-networks-security-and-privacy-comprehensive-u03yy60o6s.pdf

Online social networks security and privacy: comprehensive review and analysis

With the advent of cloud computing, it becomes increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve these data. For privacy concerns, secure searches over encrypted cloud data motivated several researches under the single owner model. However, most cloud servers in practice do not just serve one owner, instead, they support multiple owners to share the benefits brought by cloud servers. In this paper, we propose schemes to deal with secure ranked multi-keyword search in a multi-owner model. To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel Additive Order and Privacy Preserving Function family. Extensive experiments on real-world datasets confirm the efficacy and efficiency of our proposed schemes.

Secure Ranked Multi-keyword Search for Multiple Data Owners in Cloud Computing

The e-healthcare cloud system has shown its potential to improve the quality of healthcare and individuals’ quality of life. Unfortunately, security and privacy impede its widespread deployment and application. There are several research works focusing on preserving the privacy of the electronic healthcare record (EHR) data. However, these works have two main limitations. First, they only support the ‘black or white’ access control policy. Second, they suffer from the inference attack. In this paper, for the first time, we design an inference attack-resistant e-healthcare cloud system with fine-grained access control. We first propose a two-layer encryption scheme. To ensure an efficient and fine-grained access control over the EHR data, we design the first-layer encryption, where we devise a specialized access policy for each data attribute in the EHR, and encrypt them individually with high efficiency. To preserve the privacy of role attributes and access policies used in the first-layer encryption, we systematically construct the second-layer encryption. To take full advantage of the cloud server, we propose to let the cloud execute computationally intensive works on behalf of the data user without knowing any sensitive information. To preserve the access pattern of data attributes in the EHR, we further construct a blind data retrieving protocol. We also demonstrate that our scheme can be easily extended to support search functionality. Finally, we conduct extensive security analyses and performance evaluations, which confirm the efficacy and efficiency of our schemes.

Inference Attack-Resistant E-Healthcare Cloud System with Fine-Grained Access Control

Cloud computing provides abundant benefits including easy access, decreased costs and flexible resource management. For privacy concerns, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Therefore, developing a secure search service over encrypted cloud data is of paramount importance. There are several researches concerned about this problem. However, all these schemes are based on a single cloud model which has the threat of single point of failure, loss and corruption of data, loss of availability and loss of privacy. In this paper, we explore the problem of secure distributed keyword search in a multi-cloud paradigm. We first define a distributed search model. Based on this model, we propose two schemes. In scheme_I, we propose to cross-store all encrypted file slices, keywords and keys. In scheme_II, we systematically construct a keyword distributing strategy and a file distributing strategy. Further, we extend both schemes with Shamir's secret schemes to achieve better availability and robustness. Extensive experiments on real-world datasets confirm the efficacy and efficiency of our schemes.

Secure distributed keyword search in multiple clouds

Two-tiered sensor networks have been widely adopted since they offer good scalability, efficient power usage and storage saving. Storage nodes, responsible for storing data from nearby sensors and answering queries from the sink, however, are attractive to attackers. A compromised storage node would leak sensitive data to attackers and return forged or incomplete query results to the sink. In this paper, we propose SVTQ, a Secure and Verifiable Top-k Query protocol that preserves both data confidentiality and integrity of query results. To preserve data confidentiality, we propose prime aggregation whereby storage nodes can process top-k queries precisely without knowing actual data values. To preserve integrity of query results, we further propose a novel scheme called differential chain that allows the sink to verify any forged or incomplete result. Both theoretical analysis and experimental results on the real-world data set confirm the effectiveness and efficiency of SVTQ protocol.

/pdf/secure-and-verifiable-top-k-query-in-two-tiered-sensor-1a3gyw50ho.pdf

Ting Zhou

Papers

Secure Ranked Multi-keyword Search for Multiple Data Owners in Cloud Computing

Inference Attack-Resistant E-Healthcare Cloud System with Fine-Grained Access Control

Secure distributed keyword search in multiple clouds

Secure and Verifiable Top-k Query in Two-Tiered Sensor Networks