On hiding information from an oracle

Attribute-based encryption schemes with constant-size ciphertexts

Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Though the benefits are clear, it inevitably poses new security risks toward the correctness of the data in cloud. To address this problem, the proposed design allows users to audit the cloud storage with very lightweight communication and computation cost by utilizing the new cryptographic technique like homomorphic token and distributed erasure-coded data. By introducing the notion of parallel homomorphic encryption (PHE) schemes, which are encryption schemes that support computation over encrypted data via evaluation algorithms that can be efficiently executed in parallel. In addition, we can hide the function being evaluated with the MapReduce model runnable on Hadoop framework for element testing and keyword search. Analysis shows the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks and attempting to strike a balance between security, efficiency and functionality using Kerberos protocol and HDFS system.

Conventional cloud computing iot encrypted data comparative analysis method to simple encryption scheme comparison, the accuracy of the scheme comparison is not high, for more complex data encryption scheme comparison, comparative stability lower deficiencies, therefore put forward under the cloud computing environment, the Internet of things encrypted data comparison analysis research. Introducing the sliding window technology, build the Internet of things to encrypt data security evaluation mechanism, determine the iot encrypted data comparison analysis algorithm, constructing iot encrypted data comparison analysis model is presented. Run the analysis model, analyze the iot encrypted data comparison scheme, and implement the iot encrypted data comparison scheme analysis. The experimental data show that the proposed scheme is more routine than the conventional scheme analysis, and the stability of the method is maintained at 70%–90%, which is suitable for the comparison scheme analysis of the network encryption data with different difficulty coefficients. The proposed method for comparative analysis of IoT encrypted data is highly effective.

Over the years, public health has faced a large number of challenges like COVID-19. Medical cloud computing is a promising method since it can make healthcare costs lower. The computation of health data is outsourced to the cloud server. If the encrypted medical data is not decrypted, it is difficult to search for those data. Many researchers have worked on searchable encryption schemes that allow executing searches on encrypted data. However, many existing works support single-keyword search. In this article, we propose a patient-centered fine-grained attribute-based encryption scheme with multi-keyword search (CP-ABEMKS) for medical cloud computing. First, we leverage the ciphertext-policy attribute-based technique to construct trapdoors. Then, we give a security analysis. Besides, we provide a performance evaluation, and the experiments demonstrate the efficiency and practicality of the proposed CP-ABEMKS.

As the outsourcing of mobile sensitive data has been highlighted, data encryption schemes are required to protect the mobile sensitive data. Accordingly, it is necessary to develop not only a distributed index structure to efficiently manage the large amount of encrypted data, but also a query processing scheme over the encrypted data. To the best of our knowledge, there has been no research on index structure for the encrypted data. Meanwhile, the existing query processing schemes over the encrypted data can support limited types of queries. Also, without decrypting data, the existing schemes cannot support operations among data between different columns because they use different types of encryption schemes depending on their attribute types. To solve the problems, in this paper, we propose a bitmap-based distributed index structure and a query processing scheme for the encrypted data. The proposed distributed index structure guarantees data privacy preservation and performance improvement for the various types of queries. In addition, by processing a query over the encrypted data without data decryption, the proposed query processing scheme provide both high query performance and 100% accuracy while preserving the data privacy. Finally, we show from our performance analysis that our proposed index structure and query processing scheme are suitable for protecting the data privacy of the mobile sensitive data.

One of the central lines of cryptographic research is identifying the weakest assumptions required for the construction of secure primitives. In the context of group signatures the gap between what is known to be necessary (one-way functions) and what is known to be sufficient (trapdoor permutations) is quite large. In this paper, we provide the first step towards closing this gap by showing that the existence of secure group signature schemes implies the existence of secure public-key encryption schemes. Our result shows that the construction of secure group signature schemes based solely on the existence of one-way functions is unlikely. This is in contrast to what is known for standard signature schemes, which can be constructed from any one-way function.

Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases

Providing data confidentiality against malicious hosts in Shared Data Spaces

The deployment of Share Data Spaces in open, possibly hostile, environments arises the need of protecting the confidentiality of the data space content. Existing approaches focus on access control mechanisms that protect the data space from untrusted agents. The basic assumption is that the hosts (and their administrators) where the data space is deployed have to be trusted. Encryption schemes can be used to protect the data space content from malicious hosts. However, these schemes do not allow searching on encrypted data. In this paper we present a novel encryption scheme that allows tuple matching on completely encrypted tuples. Since the data space does not need to decrypt tuples to perform the search, tuple confidentiality can be guaranteed even when the data space is deployed on malicious hosts (or an adversary gains access to the host). Our scheme does not require authorised agents to share keys for inserting and retrieving tuples. Each authorised agent can encrypt, decrypt, and search encrypted tuples without having to know other agents' keys. This is beneficial inasmuch as it simplifies the task of key management. An implementation of an encrypted data space based on this scheme is described and some preliminary performance results are given.

Today, enterprises collect large amounts of and leverage the cloud to perform analytics over this data. Since the is often sensitive, enterprises would prefer to keep it confidential and to hide it even from the cloud operator. Systems such as CryptDB and Monomi can accomplish this by operating mostly on encrypted data; however, these systems rely on expensive cryptographic techniques that limit performance in true big data scenarios that involve terabytes of or more.This paper presents Seabed, a system that enables efficient analytics over large encrypted datasets. In contrast to previous systems, which rely on asymmetric encryption schemes, Seabed uses a novel, additively symmetric homomorphic encryption scheme (ASHE) to perform large-scale aggregations efficiently. Additionally, Seabed introduces a novel randomized encryption scheme called Splayed ASHE, or SPLASHE, that can, in certain cases, prevent frequency attacks based on auxiliary data.

In this paper, privacy preserving at the cloud side is enabled by adopting Homomorphic Encryption (HE) as a major solution for protecting users' sensitive data. As a Proof Of Concept (POC), we take the case of a university that is adopting the cloud as a practical method for storing and operating over its private data. The main contribution of this application is that different data are stored encrypted at the cloud side. Students' data such as name, gender, date of birth, etc are encrypted using AES and hash functions. Students' indexes and grades are encrypted homomorphically using our modified Domingo Ferrer (DF) encryption scheme [1]. The main importance of the usage of HE in this application is allowing the process over encrypted data at the cloud side such as computing encrypted students' average. Different queries are sent from the university side to the cloud side, after operating and processing over encrypted data all results are shipped back encrypted to the university where the primitive data is recovered. Cloud infrastructure is created using Apache CloudStack and different encryption schemes are implemented under Python using SageMath library.

SummaryAn error‐tolerant keyword search scheme permits to make searches on encrypted data with only an approximation of some keyword. The scheme is suitable to the case where users' searching input might not exactly match those pre‐set keywords. An error‐tolerant keyword search scheme can be generated based on a public‐key encryption scheme, in which anyone with access to a user's public key can generate the trapdoors and indexes to keywords, and only the user holding the decryption key can obtain the records it retrieves. In this paper, we first present a general framework for searching on error‐tolerant keywords based on a public‐key encryption scheme. Then, we propose a concrete scheme based on the Cramer–Shoup cryptosystem. The scheme is adaptive chosen‐ciphertext attack secure and suitable for all similarity metrics including Hamming distance metric, edit distance metric, and set difference metric. It does not require the user to construct and store anything in advance, other than the cryptosystem used to calculate the trapdoor of keywords and to encrypt data documents. Thus, our scheme tremendously eases the users' burden. What is more, our scheme is able to transform the servers' searching for error‐tolerant keywords on ciphertexts to searching for exact keywords on plaintexts. The server can use any existing approaches of exact keywords search to search plaintexts on an index table. Copyright © 2015 John Wiley & Sons, Ltd.

We show that the existence of one-way functions is necessary and sufficient for the existence of pseudo-random generators in the following sense. Let ƒ be an easily computable function such that when x is chosen randomly: (1) from ƒ(x) it is hard to recover an x1 with ƒ(x1) = ƒ(x) by a small circuit, or; (2) ƒ has small degeneracy and from ƒ(x) it is hard to recover x by a fast algorithm. From one-way functions of type (1) or (2) we show how to construct pseudo-random generators secure against small circuits or fast algorithms, respectively, and vice-versa. Previous results show how to construct pseudo-random generators from one-way functions that have special properties ([Blum, Micali 82], [Yao 82], [Levin 85], [Goldreich, Krawczyk, Luby 88]).We use the results of [Goldreich, Levin 89] in an essential way.

We give a new construction of pseudorandom generators from any one-way function. The construction achieves better parameters and is simpler than that given in the seminal work of H\aastad, Impagliazzo, Levin, and Luby [SIAM J. Comput., 28 (1999), pp. 1364--1396]. The key to our construction is a new notion of next-block pseudoentropy, which is inspired by the notion of “inaccessible entropy” recently introduced in [I. Haitner, O. Reingold, S. Vadhan, and H. Wee, Proceedings of the $41$st Annual ACM Symposium on Theory of Computing (STOC), 2009, pp. 611--620]. An additional advantage over previous constructions is that our pseudorandom generators are parallelizable and invoke the one-way function in a nonadaptive manner. Using [B. Applebaum, Y. Ishai, and E. Kushilevitz, SIAM J. Comput., 36 (2006), pp. 845--888], this implies the existence of pseudorandom generators in NC$^0$ based on the existence of one-way functions in NC$^1$.

As the demand for data sharing and complex access-control policies continues to grow, traditional encryption mechanisms, which are generally established using a Public Key Infrastructure, face the problem of massive processing overheads and huge network bandwidth consumption. Attribute-Based Encryption (ABE) schemes have been proposed as a potential means of addressing these issues and have attracted great attention in recent years. Most previous studies on ABE focus on issues such as the revocation mechanism, multi-authority, the access structure design, and traceability. However, very few studies consider the storage efficiency problem and the present study proposes a novel data deduplication scheme based on ciphertext-policy ABE with convergent encryption and block-level data. The scheme can be deployed in third-party semi-trusted environments, and not only provides flexible, fine-grained access control over encrypted data, but also allows for the in-line elimination of redundancies in order to save cloud storage space. The experimental results show that the proposed scheme has an acceptable computational overhead and provides a promising solution for real-world data cloud storage and access scenarios.