Additive Homomorphic Scheme Research Articles

Research and design of CRT-based homomorphic ciphertext database system

The cloud’s storage and query of private information have the cryptographic scholar due to the proliferation of cloud computing. In the traditional query mode, the private information stored in the cloud is at risk of being leaked. In order to solve this problem, a cloud ciphertext database system based on homomorphic encryption is a valid workaround. This paper presents a new cloud ciphertext database system model, which is based on the existing ciphertext database mode research and homomorphic properties. This paper also implements a ciphertext database system based on a CRT-based additive homomorphic scheme according to the model. Through theoretical analysis, the model is CPA-level safe and correct. The experimental results show that users can correctly query and download the data in the ciphertext database on the untrusted cloud server through the model, and it has efficiency advantages.

Authenticable Additive Homomorphic Scheme and its Application for MEC-based IoT

The integration of Internet of Things (IoT) and cloud computing are always seen as promising technologies to enhance streamlined data collection, share and exchange. Although the advances in edge computing, particularly mobile edge computing (MEC), could enhance the performance of data collection and computation via computing offloading, security and privacy impediments have made new challenges to data integrity and confidentiality, in particular when multiple edges or nodes at different locations collect IoT data. Homomorphic encryption therefore has shown promising advantages for cloud computing, offering arithmetic operations to be carried out on the encrypted data without revealing the secret key. While fully homomorphic encryption introduced by Gentry, in 2009, allows both additive and multiplicative operations, it has shown significant implementation drawbacks due to the parameters generation and memory consumption. In this work, we focus on partially homomorphic encryption, which can be efficiently computed. However, it is challenging to add authentication feature for the verification and aggregation capability. We propose a novel secure and privacy preserving authenticable homomorphic encryption (AHEC) scheme. We demonstrate an application of our AHEC scheme for MEC-based IoT systems and provide security analysis to prove that our scheme is secure against chosen plaintext attack (IND-CPA) and unforgeability (UNF) under DDH-ZN2 and Lift-DH-ZN2 assumptions. Experimental results show that our proposed scheme is efficient for practical applications.

Homomorphic Cryptosystems for Data Security in Cloud Storage

Cloud Computing enables users to use remote resources thus reduces the burden on local storage. However, the use of such services gives rise to new set of problems. The users have no control over the data which they have stored on those storages so to achieve data authentication with confidentiality is utmost important. As every user may not have that expertise so they can request for data verification task to Trusted Verifier (TV) which will be an authorized party to check the intactness of outsourced data. Since the data owner stores the data on the cloud in an encrypted format, it becomes difficult to check the integrity of the data without decrypting. But by using homomorphic encryption schemes the integrity checking can be made possible without original copy. In this paper, we have given implementation and performance details of two homomorphic encryption schemes, Rivest Shamir Adleman (RSA) and Paillier. The RSA is multiplicative homomorphic scheme where the Paillier is additive homomorphic scheme. Both the algorithms are partially homomorphic thus limited in their functions. Due to homomorphic property of these algorithms, original contents will not get revealed in the verification process. This framework will achieve authentication of data by maintaining confidentiality.

Efficient Detection for Malicious and Random Errors in Additive Encrypted Computation

Although data confidentiality is the primary security objective in additive encrypted computation applications, such as the aggregation of encrypted votes in electronic elections, ensuring the trustworthiness of data is equally important. And yet, integrity protections are generally orthogonal to additive homomorphic encryption, which enables efficient encrypted computation, due to the inherent malleability of homomorphic ciphertexts. Since additive homomorphic schemes are founded on modular arithmetic, our framework extends residue numbering to support fast modular reductions and homomorphic syndromes for detecting random errors inside homomorphic ALUs and data memories. In addition, our methodology detects malicious modifications of memory data, using keyed syndromes and block cipher-based integrity trees, which allow preserving the homomorphism of ALU operations, while enforcing non-malleability of memory data. Compared to traditional memory integrity protections, our tree-based syndrome generation and updating is parallelizable for increased efficiency, while requiring a small Trusted Computing Base for secret key storage and block cipher operations. Our evaluation shows more than 99.999 percent detection rate for random ALUs errors, as well as 100 percent detection rate of single bit-flips and clustered multiple bit upsets, for a runtime overhead between 1.2 and 5.5 percent, and a small area penalty.