Private integer comparison has been an essential computation function for many applications, including online auction, credential identification, data mining, and joint bidding. In the setting of two-party computation, two parties with private inputs (x and y) want to jointly compare them without revealing the value of those inputs to others (also known as the Millionaires’ problem) while the output should ensure correctness and preserve data privacy. The private inputs only can be revealed if they are equal, i.e., x = y. Many related works have been proposed to solve the integer comparison problem in various settings, focusing on different properties such as round and computation complexity. Most solutions decompose integers into bitwise representation and then securely evaluate the function in a Boolean circuit on encrypted bits. However, this type of solution is costly (especially for large integers) as each bit requires encryption and decryption. In this paper, we transform the private integer comparison into a block comparison problem. In particular, we employ a block vectorization mechanism to encode the private inputs into blocks. We show the security of our two-party protocol in the semi-honest model. Also, we implement the protocol to demonstrate its efficiency using block vectorization mechanism and homomorphic encryption. The experimental result proves that our proposed solution achieves high efficiency, particularly for large integer comparisons.Private integer comparison has been an essential computation function for many applications, including online auction, credential identification, data mining, and joint bidding. In the setting of two-party computation, two parties with private inputs (x and y) want to jointly compare them without revealing the value of those inputs to others (also known as the Millionaires’ problem) while the output should ensure correctness and preserve data privacy. The private inputs only can be revealed if they are equal, i.e., x = y. Many related works have been proposed to solve the integer comparison problem in various settings, focusing on different properties such as round and computation complexity. Most solutions decompose integers into bitwise representation and then securely evaluate the function in a Boolean circuit on encrypted bits. However, this type of solution is costly (especially for large integers) as each bit requires encryption and decryption. In this paper, we transform the private integer comparison into a block comparison problem. In particular, we employ a block vectorization mechanism to encode the private inputs into blocks. We show the security of our two-party protocol in the semi-honest model. Also, we implement the protocol to demonstrate its efficiency using block vectorization mechanism and homomorphic encryption. The experimental result proves that our proposed solution achieves high efficiency, particularly for large integer comparisons.

IEEE AccessJan 1, 20212 citations

Towards Robust Computation on Encrypted Data

Manoj Prabhakaran, Mike Rosulek

Lecture notes in computer scienceJan 1, 20086 citations

Secure Computation

Silvio M. Micali, Phillip Rogaway

Springer eBooksAug 11, 2007153 citations

Quantum-Adaptive Neural Encryption Algorithm for Privacy-Aware High-Performance Cloud Computing Systems

Sushil Bhattarai, Bikash Baral, Suman Thapaliya

Cloud computing has become the backbone of today's high-performance computing systems, enabling the storage, processing, and delivery of large-scale data across heterogeneous networks. However, the increasing reliance on cloud infrastructures raises important questions regarding data privacy and confidentiality, as well as efficient resource utilization. Traditional forms of encryption often struggle to find the right balance between sufficient security and efficiency in processing, particularly in dynamic cloud environments in which adaptive resource allocation is necessary. To bridge existing gaps, this work proposes a new Quantum-Adaptive Neural Encryption Algorithm (Q-ANEA), a framework that can sustain privacy of data in a high-performance cloud environment, by proposing quantum-inspired and neurocryptographic principles to combat the potentially dangerous, dynamic cloud configurations. The Q-ANEA uses the probabilistic nature of quantum operations to enhance encryption semantic, while a trained neural network can adapt to varying data patterns and loads, reducing latency and computational overhead. Benchmarks on cloud datasets were used in experiments that evaluated Q-ANEA against traditional forms of encryption (i.e., AES and RSA) with speed of encryption, integrity, and resilience to cyber demand from nefarious actors in mind. The findings show that Q-ANEA is as much as 35% faster than the fastest encryption in terms of performance, has 99.8% data integrity, and is more robust to classical and quantum attack simulations. The work further demonstrates that techniques that are quantum-inspired (quantum algorithm-based) when combined with learning algorithms (adaptive learning algorithms) can be a potentially great building block for secure, scalable, quantum powered cloud systems. In summary, the work proposes Q-ANEA as a promising option to develop a privacy-aware cloud computing system that remains high in terms of security and does not sacrifice computational efficiency, while laying the foundation to further integrate intelligent encryption deployment mechanisms for the next generation of cloud based systems.Cloud computing has become the backbone of today's high-performance computing systems, enabling the storage, processing, and delivery of large-scale data across heterogeneous networks. However, the increasing reliance on cloud infrastructures raises important questions regarding data privacy and confidentiality, as well as efficient resource utilization. Traditional forms of encryption often struggle to find the right balance between sufficient security and efficiency in processing, particularly in dynamic cloud environments in which adaptive resource allocation is necessary. To bridge existing gaps, this work proposes a new Quantum-Adaptive Neural Encryption Algorithm (Q-ANEA), a framework that can sustain privacy of data in a high-performance cloud environment, by proposing quantum-inspired and neurocryptographic principles to combat the potentially dangerous, dynamic cloud configurations. The Q-ANEA uses the probabilistic nature of quantum operations to enhance encryption semantic, while a trained neural network can adapt to varying data patterns and loads, reducing latency and computational overhead. Benchmarks on cloud datasets were used in experiments that evaluated Q-ANEA against traditional forms of encryption (i.e., AES and RSA) with speed of encryption, integrity, and resilience to cyber demand from nefarious actors in mind. The findings show that Q-ANEA is as much as 35% faster than the fastest encryption in terms of performance, has 99.8% data integrity, and is more robust to classical and quantum attack simulations. The work further demonstrates that techniques that are quantum-inspired (quantum algorithm-based) when combined with learning algorithms (adaptive learning algorithms) can be a potentially great building block for secure, scalable, quantum powered cloud systems. In summary, the work proposes Q-ANEA as a promising option to develop a privacy-aware cloud computing system that remains high in terms of security and does not sacrifice computational efficiency, while laying the foundation to further integrate intelligent encryption deployment mechanisms for the next generation of cloud based systems.

International Conference on Intelligent Innovations in Engineering and Technology (ICIIET)Dec 4, 20250 citations

A Quantum Cryptographic Scheme for an Audio File Using Quantum Data Encoding and Quantum Permutation Pad

Burcu Şanal, Erkan Güler, Muhammet Talha Kakız +1 more

Quantum computers can handle integer factorization and discrete logarithms more efficiently than their classical counterparts, which poses a threat to classical publickey cryptography. Also, quantum encryption systems and postquantum cryptography are gaining increasing attention due to the data vulnerability of classical systems. Hence, quantumnative encryption schemes are urgently needed. This work utilizes Quantum Permutation Pad (QPP), and it can be considered as an enhanced quantum extension of One Time Pad. A 2-qubit QPP is used to encrypt and decrypt an audio file using lowdepth gate permutations. The data file is segmented into 4bit blocks, each block is encoded as quantum states by using both basis and amplitude encoding, then encrypted with 2-qubit permutation circuits (depth $\leq 5$ ). The key needed for the permutation index is distributed by using the Greenberger-Horne-Zeilinger (GHZ)-based Quantum Secret Sharing scheme prior to the encoding stage. IBM Qiskit simulations showed that QPP can be a lightweight encryption scheme for NISQ devices.Quantum computers can handle integer factorization and discrete logarithms more efficiently than their classical counterparts, which poses a threat to classical publickey cryptography. Also, quantum encryption systems and postquantum cryptography are gaining increasing attention due to the data vulnerability of classical systems. Hence, quantumnative encryption schemes are urgently needed. This work utilizes Quantum Permutation Pad (QPP), and it can be considered as an enhanced quantum extension of One Time Pad. A 2-qubit QPP is used to encrypt and decrypt an audio file using lowdepth gate permutations. The data file is segmented into 4bit blocks, each block is encoded as quantum states by using both basis and amplitude encoding, then encrypted with 2-qubit permutation circuits (depth$\leq 5$). The key needed for the permutation index is distributed by using the Greenberger-Horne-Zeilinger (GHZ)-based Quantum Secret Sharing scheme prior to the encoding stage. IBM Qiskit simulations showed that QPP can be a lightweight encryption scheme for NISQ devices.

International Artificial Intelligence and Data Processing Symposium (IDAP)Sep 6, 20250 citations

Secure Computation for Big Data

Tal Malkin

Lecture notes in computer scienceJan 1, 20131 citation

\leq 5

). The key needed for the permutation index is distributed by using the Greenberger-Horne-Zeilinger (GHZ)-based Quantum Secret Sharing scheme prior to the encoding stage. IBM Qiskit simulations showed that QPP can be a lightweight encryption scheme for NISQ devices.