Secure Anonymous Query-Based Encryption for Data Privacy Preserving in Cloud

2018 ◽  
Vol 12 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Martin Konan ◽  
Wenyong Wang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Keyword Search ◽  
Data Encryption ◽  
Public Key Encryption ◽  
Sensitive Data ◽  
Security Issues ◽  
Query Privacy ◽  
Data Privacy Protection ◽  
Cloud Applications

Data privacy protection is a paramount issue in cloud applications for the last decade. In addition, data encryption, which is the primary method to impart security in clouds, is proved insufficient to guarantee data privacy protection from some security issues like homogeneity and background knowledge attacks. Therefore, it is important to provide a security mechanism that provide not only anonymous data but also anonymous continuous queries. So, this paper proposes a new scheme (Moye) that tackles this challenge by protecting queries to be linked to specific sensitive data. Specifically, the proposed solution is based on the design of a hybrid implementation of public key encryption with keyword search (PEKS) and subset membership encryption (SME) cryptosystem to enhance both data and query privacy protection. In addition, this approach provides an efficient and anonymous data processing by using an optimized k-anonymity scheme. Doing so, the authors protect searchable keywords and queries from inside and outside guessing attacks for the effectiveness of the proposed solution.

Secure Anonymous Query-Based Encryption for Data Privacy Preserving in Cloud

2021 ◽  
pp. 815-839
Author(s):  
Martin Konan ◽  
Wenyong Wang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Keyword Search ◽  
Data Encryption ◽  
Public Key ◽  
Primary Method ◽  
Sensitive Data ◽  
Security Issues ◽  
Data Privacy Protection ◽  
Cloud Applications

Data privacy protection is a paramount issue in cloud applications for the last decade. In addition, data encryption, which is the primary method to impart security in clouds, is proved insufficient to guarantee data privacy protection from some security issues like homogeneity and background knowledge attacks. Therefore, it is important to provide a security mechanism that provide not only anonymous data but also anonymous continuous queries. So, this paper proposes a new scheme (Moye) that tackles this challenge by protecting queries to be linked to specific sensitive data. Specifically, the proposed solution is based on the design of a hybrid implementation of public key encryption with keyword search (PEKS) and subset membership encryption (SME) cryptosystem to enhance both data and query privacy protection. In addition, this approach provides an efficient and anonymous data processing by using an optimized k-anonymity scheme. Doing so, the authors protect searchable keywords and queries from inside and outside guessing attacks for the effectiveness of the proposed solution.

scholarly journals Privacy and Security in Data-Driven Urban Mobility

2019 ◽  
pp. 106-128 ◽  
Author(s):  
Rajendra Akerkar
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Evidence Base ◽  
Urban Mobility ◽  
Privacy And Security ◽  
Security Issues ◽  
Wide Range ◽  
Beneficial Uses ◽  
Smart Mobility ◽  
Data Privacy Protection

A wide range of smart mobility technologies are being deployed within urban environment. These technologies generate huge quantities of data, much of them in real-time and at a highly granular scale. Such data about mobility, transport, and citizens can be put to many beneficial uses and, if shared, for uses beyond the system and purposes for which they were generated. Jointly, these data create the evidence base to run mobility services more efficiently, effectively, and sustainably. However, generating, processing, analyzing, sharing, and storing vast amounts of actionable data also raises several concerns and challenges. For example, data privacy, data protection, and data security issues arise from the creation of smart mobility. This chapter highlights the various privacy and security concerns and harms related to the deployment and use of smart mobility technologies and initiatives, and makes suggestions for addressing apprehensions about and harms arising from data privacy, protection, and security issues.

scholarly journals The issues connected with the anonymization of medical data. Part 1. The introduction to the anonymization of medical data. Ensuring the protection of sensitive information with the use of such methods as f(a) and f(a,b)

2014 ◽  
Vol 8 (1) ◽  
pp. 13-21 ◽  
Author(s):  
ARKADIUSZ LIBER
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Medical Data ◽  
Medical Documentation ◽  
Sensitive Information ◽  
Sensitive Data ◽  
New Methods ◽  
Anonymized Data ◽  
Data Privacy Protection

Introduction: Medical documentation must be protected against damage or loss, in compliance with its integrity and credibility and the opportunity to a permanent access by the authorized staff and, finally, protected against the access of unauthorized persons. Anonymization is one of the methods to safeguard the data against the disclosure.Aim of the study: The study aims at the analysis of methods of anonymization, the analysis of methods of the protection of anonymized data and the study of a new security type of privacy enabling to control sensitive data by the entity which the data concerns.Material and methods: The analytical and algebraic methods were used.Results: The study ought to deliver the materials supporting the choice and analysis of the ways of the anonymization of medical data, and develop a new privacy protection solution enabling the control of sensitive data by entities whom this data concerns.Conclusions: In the paper, the analysis of solutions of data anonymizing used for medical data privacy protection was con-ducted. The methods, such as k-Anonymity, (X,y)- Anonymity, (a,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 to control sensitive data by their owners was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the anonimized data protection was conducted, in particular the methods of: d-Presence, e-Differential Privacy, (d,g)-Privacy, (a,b)-Distributing Privacy and protections against (c,t)-Isolation were analyzed. The author introduced a new solution of the controlled protection of privacy. The solution is based on marking a protected field and multi-key encryption of the sensitive value. The suggested way of fields marking is in accordance to the XML standard. For the encryption (n,p) different key cipher was selected. To decipher the content the p keys of n is used. The proposed solution enables to apply brand new methods for the control of privacy of disclosing sensitive data.

scholarly journals The New Method of Sensor Data Privacy Protection for IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yue Wu ◽  
Liangtu Song ◽  
Lei Liu
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Access ◽  
Data Encryption ◽  
New Method ◽  
Sensor Data ◽  
Gateway Node ◽  
Data Access Control ◽  
Protection Method ◽  
Data Privacy Protection

This article introduces the new method of sensor data privacy protection method for IoT. Asymmetric encryption is used to verify the identity of the gateway by the sensor. The IoT gateway node verifies the integrity and source of the data, then creates a block, and submits the block chain transaction. In order to avoid tracking the source of the data, a ring signature is used to anonymize the gateway transaction. The proxy re-encryption method realizes the sharing of encrypted data. On the basis of smart contracts, attribute-based data access control allows decentralized applications to finely control data access. Through experiments, the effects of sensor/gateway verification, transaction signatures, and sensor data encryption on performance are discussed. The results show that transaction delays are all controlled within a reasonable range. The system performance achieved by this method is also relatively stable.

A federated interpretable scorecard and its application in credit scoring

2021 ◽  
pp. 2142009
Author(s):  
Fanglan Zheng ◽  
Erihe ◽  
Kun Li ◽  
Jiang Tian ◽  
Xiaojia Xiang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Credit Scoring ◽  
Training Session ◽  
Model Performance ◽  
Parameter Tuning ◽  
Kolmogorov Smirnov ◽  
Data Privacy Protection ◽  
Tuning Process ◽  
Positive Coefficients

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

Is There a Better Option than the Data Transfer Model to Protect Data Privacy?

2019 ◽  
Vol 42 (2) ◽  
Author(s):  
Alan Toy ◽  
Gehan Gunasekara
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Transfer ◽  
Business Environment ◽  
Transfer Model ◽  
Privacy Rights ◽  
Choice Of Law ◽  
Privacy Laws ◽  
Data Privacy Protection ◽  
Extraterritorial Jurisdiction

The data transfer model and the accountability model, which are the dominant models for protecting the data privacy rights of citizens, have begun to present significant difficulties in regulating the online and increasingly transnational business environment. Global organisations take advantage of forum selection clauses and choice of law clauses and attention is diverted toward the data transfer model and the accountability model as a means of data privacy protection but it is impossible to have confidence that the data privacy rights of citizens are adequately protected given well known revelations regarding surveillance and the rise of technologies such as cloud computing. But forum selection and choice of law clauses no longer have the force they once seemed to have and this opens the possibility that extraterritorial jurisdiction may provide a supplementary conceptual basis for championing data privacy in the globalised context of the Internet. This article examines the current basis for extraterritorial application of data privacy laws and suggests a test for increasing their relevance.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

Dealing with Data Privacy Protection

2003 ◽  
Author(s):  
Fritz Grupe ◽  
William Kuechler ◽  
Scott Sweeney
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Data Privacy Protection

Oracle Data Privacy Protection System of Virtual Database

2021 ◽  
pp. 353-363
Author(s):  
Shenglong Liu ◽  
Hongbin Zhu ◽  
Tao Zhao ◽  
Heng Wang ◽  
Xianzhou Gao ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Protection System ◽  
Virtual Database ◽  
Data Privacy Protection
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