Differential Privacy Protection with Group Onion Routing based on AI-based URL Classification

2020 ◽  
Author(s):  
I-Hsien Liu ◽  
Yung-Lin Chang ◽  
Jung-Shian Li ◽  
Chuan-Gang Liu
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Onion Routing

scholarly journals The issues connected with the anonymization of medical data. Part 2. Advanced anonymization and anonymization controlled by owner of protected sensitive data

2014 ◽  
Vol 8 (2) ◽  
pp. 13-24 ◽  
Author(s):  
Arkadiusz Liber
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Data Access ◽  
Medical Data ◽  
Medical Documentation ◽  
Research Review ◽  
Sensitive Data ◽  
Data Anonymization ◽  
Data Access Control ◽  
Anonymized Data

Introduction: Medical documentation ought to be accessible with the preservation of its integrity as well as the protection of personal data. One of the manners of its protection against disclosure is anonymization. Contemporary methods ensure anonymity without the possibility of sensitive data access control. it seems that the future of sensitive data processing systems belongs to the personalized method. In the first part of the paper k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, and (k,e)-Anonymity methods were discussed. these methods belong to well - known elementary methods which are the subject of a significant number of publications. As the source papers to this part, Samarati, Sweeney, wang, wong and zhang’s works were accredited. the selection of these publications is justified by their wider research review work led, for instance, by Fung, Wang, Fu and y. however, it should be noted that the methods of anonymization derive from the methods of statistical databases protection from the 70s of 20th century. Due to the interrelated content and literature references the first and the second part of this article constitute the integral whole.Aim of the study: The analysis of the methods of anonymization, the analysis of the methods of protection of anonymized data, the study of a new security type of privacy enabling device to control disclosing sensitive data by the entity which this data concerns.Material and methods: Analytical methods, algebraic methods.Results: Delivering material supporting the choice and analysis of the ways of anonymization of medical data, developing a new privacy protection solution enabling the control of sensitive data by entities which this data concerns.Conclusions: In the paper the analysis of solutions for data anonymization, to ensure privacy protection in medical data sets, was conducted. the methods of: k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, lKc-Privacy, l-Diversity, (X,y)-linkability, t-closeness, confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions of controlling sensitive data by their owner was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the protection of anonymized data was included. In particular, the methods of: δ-Presence, e-Differential Privacy, (d,γ)-Privacy, (α,β)-Distributing Privacy and protections against (c,t)-isolation were analyzed. Moreover, the author introduced a new solution of the controlled protection of privacy. the solution is based on marking a protected field and the multi-key encryption of sensitive value. The suggested way of marking the fields is in accordance with Xmlstandard. For the encryption, (n,p) different keys cipher was selected. to decipher the content the p keys of n were used. The proposed solution enables to apply brand new methods to control privacy of disclosing sensitive data.

scholarly journals Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao ◽  
Shubo Liu ◽  
Xingxing Xiong ◽  
Zhaohui Cai
Keyword(s):  
Time Series ◽  
Privacy Protection ◽  
Large Scale ◽  
Differential Privacy ◽  
Time Series Data ◽  
Sliding Window ◽  
Data Publishing ◽  
Series Data ◽  
Data Publication ◽  
Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

scholarly journals Learning With Differential Privacy

2021 ◽  
pp. 372-395
Author(s):  
Poushali Sengupta ◽  
Sudipta Paul ◽  
Subhankar Mishra
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Intrusion Detection Systems ◽  
Sensitive Information ◽  
Detection Systems ◽  
Trade Offs ◽  
Personal Level ◽  
Encryption Decryption ◽  
Individual Trees ◽  
Prevention Methods

The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, its application in privacy protection and leakage of information, a comparative discussion on the current research approaches in this field, its utility in the real world as well as the trade-offs will be discussed.

scholarly journals Protecting patient privacy in survival analyses

2019 ◽  
Vol 27 (3) ◽  
pp. 366-375
Author(s):  
Luca Bonomi ◽  
Xiaoqian Jiang ◽  
Lucila Ohno-Machado
Keyword(s):  
Survival Analysis ◽  
Biomedical Research ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Future Research ◽  
Patient Privacy ◽  
Privacy Risk ◽  
Survival Curves ◽  
Healthcare Applications ◽  
Survival Analyses

Abstract Objective Survival analysis is the cornerstone of many healthcare applications in which the “survival” probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis. Materials and Methods We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model. Results We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves. Discussion The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications. Conclusion The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.

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