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.

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.

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.

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.

Dealing with Data Privacy Protection

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

Study and Survey on Blockchain Privacy and Security Issues

2021 ◽  
pp. 169-191
Author(s):  
Sourav Banerjee ◽  
Debashis Das ◽  
Manju Biswas ◽  
Utpal Biswas
Keyword(s):  
Data Storage ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Privacy And Security ◽  
Unauthorized Access ◽  
Security Issues ◽  
Blockchain Technology ◽  
Targeted Attacks ◽  
Wide Range ◽  
Financial Transactions

Blockchain-based technology is becoming increasingly popular and is now used to solve a wide range of tasks. And it's not all about cryptocurrencies. Even though it's based on secure technology, a blockchain needs protection as well. The risks of exploits, targeted attacks, or unauthorized access can be mitigated by the instant incident response and system recovery. Blockchain technology relies on a ledger to keep track of all financial transactions. Ordinarily, this kind of master ledger would be a glaring point of vulnerability. Another tenet of security is the chain itself. Configuration flaws, as well as insecure data storage and transfers, may cause leaks of sensitive information. This is even more dangerous when there are centralized components within the platform. In this chapter, the authors will demonstrate where the disadvantages of security and privacy in blockchain are currently and discuss how blockchain technology can improve these disadvantages and outlines the requirements for future solution.

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

Research on Uncertain Data Privacy Protection Based on K-Anonymity

2012 ◽  
Vol 4 (15) ◽  
pp. 140-149 ◽  
Author(s):  
Yang Jing ◽  
Ren Xiangmin ◽  
Zhang Jianpei ◽  
Wang Kechao
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Uncertain Data ◽  
Data Privacy Protection
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