[CLOSED] Call for Papers: Special Section on Advances in Emerging Privacy-Preserving Computing
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Submissions Due: 15 June 2022
Machine learning and cloud computing have dramatically increased the utility of data. These technologies facilitate our life and provide smart and intelligent services. Notably, machine learning algorithms need to learn from massive training data to improve accuracy. Hence, data is the core component of machine learning and plays an important role. Cloud computing is a new computing model that provides on-demand services, such as data storage, computing power, and infrastructure. Data owners are allowed to outsource their data to cloud servers, but will lose direct control of their data. The rising trend in data breach shows that privacy and security have been major issues in machine learning and cloud computing.
Computation over unencrypted sensitive data may compromise the confidentiality of data and suffer various security attacks, such as identity theft and fraud. To regulate data collecting and releasing, the European Union (EU) announced the strict privacy protection policy called General Data Protection Regulation (GDPR), which was put into effect in 2018 and applied to enhance individuals’ control and rights over their personal data. Traditional cryptographic methods (such as DES, AES, and hash) can be applied to protect data confidentiality, but do not support computation over encrypted data. Therefore, these methods are not suitable for machine learning and cloud computing.
To address the requirements for computation in machine learning and cloud computing, new computation techniques must be developed. Privacy-preserving computation techniques were proposed to provide protection on sensitive data and support secure computation over encrypted data. Since data remains encrypted or opaqued during computation, it is immune to being collected and abused. Some methods have been proposed to implement privacy-preserving computation, such as homomorphic encryption and secure multi-party computation, but these methods are computationally costly. Therefore, it is urgent to develop new privacy-preserving computation techniques to protect data confidentiality and support efficient computation over encrypted data.
Relevant topics of interest to this special section include (but are not limited to):
Foundations and applications of privacy-preserving computing: secure multi-party computation, zero-knowledge, oblivious transfer, security models, etc.
Real-world applied differential privacy, such as e-health, image sharing, and location-based services
Homomorphic encryption (HE): novel applications of HE, implementations in hardware and software of HE, etc.
Functional encryption (FE): novel applications of FE, efficient constructions for concrete functions, implementations in hardware and software of FE, etc.
Privacy-preserving computing and machine learning in real-world computing applications
Design and implementation of trusted platform modules (TPMs): TPM-based anonymous authentication, signature, encryption, identity management, etc.
Applied trusted execution environments (TEEs): TEE-based privacy techniques, vulnerability and countermeasures of TEE, distributed TEE, decentralized TEE, etc.
Purely theoretical and algorithm development papers are not in scope of this special section, and every submission must have a clear connection to realistic computing applications, implementations, or simulations, as well as comparisons. Communication protocols, theoretical cryptography algorithms, and contributions outside the scope of the journal will not be considered.
Deadline for submissions: June 15, 2022
First decision (accept/reject/revise, tentative): August 24, 2022
Submission of revised papers: November 16, 2022
Notification of final decision (tentative): December 28, 2022
Journal publication (tentative): first half of 2023
For author information and guidelines on submission criteria, please visit the TETC Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.