Big data analytics over encrypted datasets with seabed

This work presents the employment of graph theory for cryptographic applications. A Cartesian product graph (CPG) is used to propose new symmetric encryption schemes. On proposed encryption schemes, the ciphertexts of the plaintexts are sent to the receiver entity as the CPGs. The CPG is created based on two graphs G and H that are generated depending on the random choices of the plaintext bits. These bits are used as vertices of graphs G and H. The purpose of using the CPG is to increase the security level of the symmetric encryption schemes in comparison with other encryption schemes. Two study cases of the proposed symmetric Cartesian product graphic encryption (SCPGE) schemes are presented as new experimental results. The security issues of the proposed SCPGE schemes are determined. The SCPGE schemes consider as new insights for more secure communications.

The need of security to protect the data through networks has become of vital importance and critical for many sensor network applications. There are several security schemes implemented using hardware or software trying to solve the problem of security in WSN by taking into consideration the limitations of sensors (bandwidth and energy), the majority of them are symmetric key encryption schemes and some others are asymmetric encryption schemes is not recommended to be used because of high time complexity and consumption demand. In this study we compare the time complexity and power consumption between software and hardware implementation using RSA algorithm. Our simulation shows that usage of hardware security could improve time efficiency and decrease the power consumption, so the strong cryptography can be implemented in WSNs security.

Asymmetric ciphers are widely used to ensure the confidentiality of data transmission via insecure channels. These cryptosystems allow the interacting parties to create a shared secret key for a symmetric cipher in such a way that an eavesdropper gets no information useful for cryptanalysis. Network security protocols that use asymmetric ciphers include TLS, S/MIME, OpenPGP, Tor, and many others. Some of the asymmetric encryption schemes are homomorphic, that is, that they allow calculations on encrypted data to be performed without preliminary decryption. The aforesaid property makes possible using these cryptosystems not only for symmetric key establishment but also in several areas of application, in particular in secret voting protocols and cloud computing. The matrix-based knapsack cipher is a new additively homomorphic asymmetric encryption scheme, which is based on the properties of isomorphic transformations of the inner direct product of diagonal subgroups of a general linear group over a Galois field. Unlike classic knapsack encryption schemes, the cryptographic strength of this cipher depends on the computational complexity of the multidimensional discrete logarithm problem. Despite some useful properties, further research into the cryptographic strength of the matrix-based knapsack cipher has found serious drawbacks inherent in this cryptographic scheme. In the given paper an improved polynomial-time plaintext-recovery attack on the matrix-based knapsack cipher is proposed. Applying this cryptanalytic method requires only public information and has time complexity O(t1.34), where t denotes the decryption time of the attacked cryptosystem. The aforementioned attack is more productive and easier to implement in software in comparison with the original one. The advantages of the proposed method are due to using in its algorithm the simple and relatively fast matrix trace operation instead of more complex and slower transformations.

In this paper, we propose a new “full public verifiability” concept for hybrid public-key encryption schemes. We also present a new hybrid public-key encryption scheme that has this feature, which is based on the decisional bilinear Diffie-Hellman assumption. We have proven that the new hybrid public-key encryption scheme is secure against adaptive chosen ciphertext attack in the standard model. The “full public verifiability” feature means that the new scheme has a shorter ciphertext and reduces the security requirements of the symmetric encryption scheme. Therefore, our new scheme does not need any message authentication code, even when the one-time symmetric encryption scheme is passive attacks secure. Compared with all existing publickey encryption schemes that are secure to the adaptive chosen ciphertext attack, our new scheme has a shorter ciphertext, efficient tight security reduction, and fewer requirements (if the symmetric encryption scheme can resist passive attacks).

Image fusion is a popular method which provides better quality fused image for interpreting the image data. In this paper, color image fusion using wavelet transform is applied for securing data through asymmetric encryption scheme and image hiding. The components of a color image corresponding to different wavelengths (red, green, and blue) are fused together using discrete wavelet transform for obtaining a better quality retrieved color image. The fused color components are encrypted using amplitude- and phase-truncation approach in Fresnel transform domain. Also, the individual color components are transformed into different cover images in order to result disguising information of input image to an attacker. Asymmetric keys, Fresnel propagation parameters, weighing factor, and three cover images provide enlarged key space and hence enhanced security. Computer simulation results support the idea of the proposed fused color image encryption scheme.

With the wide adoption of cloud computing paradigm, it is important to develop appropriate techniques to protect client data privacy in the cloud. Encryption is one of the major techniques that could be used to achieve this goal. However, data encryption at the rest alone is insufficient for secure cloud computation environments. There is also the need for efficient techniques to carry out computation over encrypted data. Fully homomorphic encryption (FHE) and garbled circuits are naturally used to process encrypted data without leaking any information about the data. However, existing FHE schemes are inefficient for processing large amount of data in cloud and garbled circuits are one time programs and cannot be reused. Based on quaternion/octonion algebra and Jordan algebra over finite rings \(\mathbb {Z}_q\), this paper designs efficient fully homomorphic symmetric key encryption (FHE) schemes without bootstrapping (that is, noise-free FHE schemes) that are secure in the weak ciphertext-only security model assuming the hardness of solving multivariate quadratic equation systems and solving univariate high degree polynomial equation systems in \(\mathbb {Z}_q\). The FHE scheme designed in this paper is sufficient for privacy preserving computation in cloud.

Most existing symmetric searchable encryption schemes aim at allowing a user to outsource her encrypted data to a cloud server and delegate the latter to search on her behalf. These schemes do not qualify as a secure and scalable solution for the multiparty setting, where users outsource their encrypted data to a cloud server and selectively authorize each other to search. Due to the possibility that the cloud server may collude with some malicious users, it is a challenge to have a secure and scalable multiparty searchable encryption (MPSE) scheme. This is shown by our analysis on the Popa-Zeldovich scheme, which says that an honest user may leak all her search patterns even if she shares only one of her documents with another malicious user. Based on our analysis, we present a new security model for MPSE by considering the worst case and average-case scenarios, which capture different server-user collusion possibilities. We then propose a MPSE scheme by employing the bilinear property of Type-3 pairings and prove its security based on the bilinear Diffie-Hellman variant and symmetric external Diffie-Hellman assumptions in the random oracle model.

Although searchable encryption schemes allow secure search over the encrypted data, they mostly support conventional Boolean keyword search, without capturing any relevance of the search results. This leads to a large amount of post-processing overhead to find the most matching documents and causes an unnecessary communication cost between the servers and end-users. Such problems can be addressed efficiently using a ranked search system that retrieves the most relevant documents. However, existing state-of-the-art solutions in the context of Searchable Symmetric Encryption (SSE) suffer from either (a) security and privacy breaches due to the use of Order Preserving Encryption (OPE) or (b) non-practical solutions like using the two non-colluding servers. In this paper, we present a generic solution for multi-keyword ranked search over the encrypted cloud data. The proposed solution can be applied over different symmetric searchable encryption schemes. To demonstrate the practicality of our technique, in this paper we leverage the Oblivious Cross Tags (OXT) protocol of Cash et al. (2013) due to its scalability and remarkable flexibility to support different settings. Our proposed scheme supports the multi-keyword search on Boolean, ranked and limited range queries while keeping all of the OXT’s properties intact. The key contribution of this paper is that our scheme is resilience against all common attacks that take advantage of OPE leakage while only a single cloud server is used. Moreover, the results indicate that using the proposed solution the communication overhead decreases drastically when the number of matching results is large.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets to cloud. To preserve privacy, the datasets are usually encrypted before outsourcing. However, the common practice of encryption makes the effective utilization of the data difficult. For example, it is difficult to search the given keywords in encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users' retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we propose ECSED, a novel semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. ECSED uses two cloud servers. One is used to store the outsourced datasets and return the ranked results to data users. The other one is used to compute the similarity scores between the documents and the query and send the scores to the first server. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. We employ the multi-keyword ranked search over encrypted cloud data as our basic frame to propose two secure schemes. The experiment results based on the real world datasets show that the scheme is more efficient than previous schemes. We also prove that our schemes are secure under the known ciphertext model and the known background model.

A growing number of Industrial Internet of Things (IIoT) devices and services collect massive time series data related to production, monitoring and maintenance. To provide ubiquitous access, scalability and sharing possibilities, the IIoT applications utilize the cloud to store collected data streams. However, secure storing of the massive and continuously generated data poses significant privacy risks, including data breaches for IIoT applications. Alongside, we need to protect the utility of the data streams by allowing benign services to access and run analytics securely and selectively.This paper introduces SmartCrypt, a data storing and sharing system that supports analytics over the encrypted time series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data. Additionally, SmartCrypt guarantees data confidentiality in the presence of unauthorized parties by allowing end-to-end encryption using a novel symmetric homomorphic encryption scheme. We perform exhaustive experiments on a real-world dataset primarily to assess the feasibility of SmartCrypt for secure storing and sharing of IIoT data streams. The results show that SmartCrypt reduces more than 17% query time, 32% range query time and improves 9% throughput over the best performed scheme in the state-of-the-art.

The existing key aggregate searchable encryption (KASE) schemes allow searches on the encrypted dataset using a single query trapdoor, with a feature to delegate the search rights of multiple files using a constant-size key. However, the limitation of the existing KASE schemes is they only support the exact keyword match $$ (Keyword = Value)$$ search query for a single query keyword. In addition, to perform a conjunctive keyword search using the existing KASE schemes, the user requires to submit different trapdoors for each individual keyword to the server. Considering the existence of numeric keywords, the KASE should support different types of query such as range query$$ (Value_1 \le Keyword \le Value_2) $$, comparison query($$ (Keyword \ge Value)$$ or $$ (Keyword \le Value)$$), and sort query. Therefore, a novel KASE scheme is proposed in this paper that supports the conjunctive range and sort query (i.e., CRSQ-KASE) on the encrypted dataset and enhances the query expressiveness of the existing KASE schemes. The proposed CRSQ-KASE scheme supports search among different keyword fields considering both numeric and non-numeric keywords. The user can search over shared encrypted dataset by giving arbitrary conjunctive queries $$ (Keyword_1 = Value_1\wedge Keyword_2 \le Value_2 \wedge \cdots \wedge Value_{n_1} \le Keyword_n \le Value_{n_2}) $$ using a single trapdoor. Furthermore, in the multi-owner setting, the user can search over multiple users’ documents set using a single query trapdoor. The theoretical, empirical, and security analyses show that the proposed CRSQ-KASE scheme performs better than the existing KASE schemes. To the best of our knowledge, the CRSQ-KASE is the first attempt that efficiently supports multi-dimensional, conjunctive keyword searches on the multi-owner encrypted dataset and gives the sorted search result.

Machine learning for Big Data analytics in plants.

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.

Wireless Sensor Networks are installed in hostile areas. The security issues in wireless sensor networks are very important. Getting secure links between nodes is a challenging problem in WSNs. They are more vulnerable to security attacks than wired networks. In order to protect the sensitive data in WSN can be protected using secret keys to encrypt the exchanged messages between communicating nodes. Key management is essential for many security services such as confidentiality and authentication. The symmetric or asymmetric key cryptography or Trusted-server schemes are used to solve this problem. Asymmetric key cryptography increases network security but it increases computational, memory, and energy overhead. Symmetric key cryptography provides less security and it is efficient key management scheme. Trusted server schemes use key management server. Because there is usually no trusted infrastructure it is not very suitable for sensor networks. In this paper, we have proposed Mobile Agent (MA) Based Key Distribution (MAKD). In MAKD, Mobile Agents are used for dissemination of public keys and update of shared keys. Each sensor node constructs different symmetric keys with its neighbors, and communication security is achieved by data encryption and mutual authentication with these keys. Simulation results show that MAKD is scalable and with less communication overhead.