• Corpus ID: 254246231

Semantics-Preserved Distortion for Personal Privacy Protection in Information Management

  title={Semantics-Preserved Distortion for Personal Privacy Protection in Information Management},
  author={Jiajia Li and Letian Peng and P. Wang and Zuchao Li and Xueyi Li and Haihui Zhao},



Privacy-Preserving Deep Learning and Inference

  • M. RiaziF. Koushanfar
  • Computer Science
    2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
  • 2018
A systemization of knowledge of the recent progress made in addressing the crucial problem of deep learning on encrypted data is provided, including a summary of the state-of-the-art, comparison of the existing solutions, as well as future challenges and opportunities.

Semantic Disclosure Control: semantics meets data privacy

Semantic Disclosure Control (SeDC) is an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data.

A Blockchain-based Privacy-Preserving Recommendation Mechanism

A privacy-preserving recommendation mechanism based on blockchain is proposed that shows better performance on privacy preservation while maintaining desirable recommendation accuracy when compared with the baseline and is combined with Inter-Planetary File System with blockchain to greatly improve the communication efficiency.

SDRM-LDP: A Recommendation Model Based on Local Differential Privacy

A short-term dynamic recommendation model based on local differential privacy (SDRM-LDP) is proposed that considers that an attacker uses nonprivate items to derive privacy items and randomly replaces the original data in the same category.

Towards a More Reliable Privacy-preserving Recommender System

PPD-DL: Privacy-Preserving Decentralized Deep Learning

PPD-DL includes two non-collusion cloud servers, one for computing clients’ local update safely based on homomorphic encryption, the other for maintaining a global model without the details of individual contribution.

Privacy-preserving Recommendation for Location-based Services

The novelty of the proposed protocol is the design of a commercially valuable privacy recommendation mechanism that could benefit both consumers and service providers on LBS.

A Lightweight Three-Factor Anonymous Authentication Scheme With Privacy Protection for Personalized Healthcare Applications

A lightweight three-factor anonymous authentication scheme with forward secrecy for personalized healthcare applications using only the lightweight cryptographic primitives that can not only reduce execution time as compared with the most effective related schemes, but also achieve more security and functional features.

Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

A chaotic based RDT approach for privacy-preserving data mining (PPDM) in recommendation system using RDT parameter values generated locally and because of this, prior sharing of the parameter values for the recovery process will not be necessary.