Privacy-Preserving Traceable Functional Encryption Scheme for Inner Product

Główne pojęcia

The authors propose a privacy-preserving traceable functional encryption scheme for inner product that allows users to anonymously obtain secret keys from the key generation center while enabling the tracer to identify the key holder's identity if required.

Streszczenie

The content presents a new primitive called privacy-preserving traceable functional encryption for inner product (PPTFE-IP) that balances the relationship between privacy and traceability in functional encryption for inner product (FE-IP).
Key highlights:

Each user can anonymously obtain a secret key from the key generation center without revealing their identity.
The tracer can trace the identity of a key holder if required.
The scheme is formally defined in terms of definitions, security models, and security proofs.
The key generation process is privacy-preserving, where the key generation center does not know the user's identity.
The scheme prevents user collusion attacks by binding all elements of the secret key together.
The security of the scheme is formally reduced to well-known complexity assumptions.
The implementation and efficiency evaluation of the scheme are provided.

Statystyki

Each user can anonymously obtain a secret key from the key generation center without releasing anything about his/her identity.
The tracer can trace the key holder's identity if required.
The scheme is formally treated, in terms of definitions, security models and security proof.

Cytaty

"To balance privacy and accountability, we propose the concept of privacy-preserving traceable functional encryption for inner product (PPTFE-IP) and give a concrete construction."
"Our scheme provides the following features: (1) To prevent key sharing, a user's key is bound with both his/her identity and a vector; (2) The key generation center (KGC) and a user execute a two-party secure computing protocol to generate a key without the former knowing anything about the latter's identity; (3) Each user can verify the correctness of his/her key; (4) A user can calculate the inner product of the two vectors embedded in his/her key and in a ciphertext; (5) Only the tracer can trace the identity embedded in a key."

Kluczowe wnioski z

Privacy-Preserving Traceable Functional Encryption for Inner Product

by Muyao Qiu,Ji... o arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04861.pdf

Głębsze pytania

How can the proposed PPTFE-IP scheme be extended to support more complex functionalities beyond inner product computation

The proposed PPTFE-IP scheme can be extended to support more complex functionalities beyond inner product computation by incorporating additional operations and functions into the encryption and decryption processes. For example, the scheme could be modified to handle more advanced mathematical operations such as matrix multiplication, polynomial evaluation, or even more intricate computations used in machine learning algorithms. By adapting the key generation, encryption, and decryption algorithms to accommodate these operations, the scheme can be enhanced to provide a broader range of functionalities while still maintaining privacy and traceability features.

What are the potential limitations or drawbacks of the privacy-preserving key generation protocol, and how can they be addressed

One potential limitation of the privacy-preserving key generation protocol is the computational overhead and communication complexity involved in the secure two-party computing protocol between the user and the Key Generation Center (KGC). This could result in increased processing time and resource requirements, especially in scenarios with a large number of users or complex encryption operations. To address this, optimizations such as using more efficient cryptographic techniques, parallelizing key generation processes, or implementing hardware acceleration can be considered to improve the protocol's efficiency and scalability.
Another drawback could be the potential vulnerability to side-channel attacks or collusion between users to manipulate the key generation process. To mitigate these risks, additional security measures such as incorporating secure multi-party computation protocols, implementing robust authentication mechanisms, and regularly updating cryptographic algorithms can enhance the overall security of the key generation protocol.

What are the broader implications of balancing privacy and traceability in the context of functional encryption, and how might this approach be applied to other cryptographic primitives

Balancing privacy and traceability in functional encryption has significant implications for enhancing data security and accountability in various applications. By ensuring that users can securely perform computations on encrypted data while still allowing for traceability in case of malicious behavior, the approach offers a comprehensive solution for protecting sensitive information while maintaining transparency and auditability.
This approach can be applied to other cryptographic primitives to achieve a similar balance between privacy and traceability. For example, in attribute-based encryption (ABE), combining privacy-preserving techniques with traceability features can enable secure data sharing based on specific attributes while allowing for accountability in case of data breaches. Similarly, in secure multiparty computation (MPC), integrating privacy-preserving mechanisms with traceability can facilitate collaborative data analysis while ensuring the integrity of the computation process. Overall, the concept of balancing privacy and traceability can be a fundamental principle in designing secure cryptographic systems across various domains.