scholarly journals Privacy Protection Method for Multiple Sensitive Attributes Based on Strong Rule

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Tong Yi ◽  
Minyong Shi
Keyword(s):  
Association Rules ◽  
Privacy Protection ◽  
Background Knowledge ◽  
Data Publishing ◽  
High Quality ◽  
New Model ◽  
Attack Model ◽  
Protection Method ◽  
Sensitive Attribute ◽  
Almost All

At present, most studies on data publishing only considered single sensitive attribute, and the works on multiple sensitive attributes are still few. And almost all the existing studies on multiple sensitive attributes had not taken the inherent relationship between sensitive attributes into account, so that adversary can use the background knowledge about this relationship to attack the privacy of users. This paper presents an attack model with the association rules between the sensitive attributes and, accordingly, presents a data publication for multiple sensitive attributes. Through proof and analysis, the new model can prevent adversary from using the background knowledge about association rules to attack privacy, and it is able to get high-quality released information. At last, this paper verifies the above conclusion with experiments.

scholarly journals Uncertain Data Privacy Protection Based on K-Anonymity via Anatomy

2012 ◽  
Vol 6-7 ◽  
pp. 64-69 ◽  
Author(s):  
Xiang Min Ren ◽  
Jing Yang ◽  
Jian Pei Zhang ◽  
Zong Fu Jia
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Uncertain Data ◽  
Information Leakage ◽  
Data Publishing ◽  
Data Set ◽  
Influence Matrix ◽  
Sensitive Attribute ◽  
Influence Degree ◽  
Equivalent Class

In traditional database domain, k-anonymity is a hotspot in data publishing for privacy protection. In this paper, we study how to use k-anonymity in uncertain data set, use influence matrix of background knowledge to describe the influence degree of sensitive attribute produced by QI attributes and sensitive attribute itself, use BK(L,K)-clustering to present equivalent class with diversity, and a novel UDAK-anonymity model via anatomy is proposed for relational uncertain data. We will extend our ideas for handling how to solve privacy information leakage problem by using UDAK-anonymity algorithms in another paper.

Anatomy: Uncertain Data k-Anonymity Privacy Protection Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 1689-1692 ◽  
Author(s):  
Xiangmin Ren ◽  
Boxuan Jia ◽  
Kechao Wang
Keyword(s):  
Privacy Protection ◽  
Uncertain Data ◽  
Background Knowledge ◽  
Research Direction ◽  
Important Research ◽  
Influence Matrix ◽  
Sensitive Attribute ◽  
Influence Degree ◽  
Equivalent Class ◽  
Important Research Direction

Uncertain data management has become an important research direction and a hot area of research. This paper proposes an UDAK-anonymity algorithm via anatomy for relational uncertain data. Uncertain data influence matrix based on background knowledge is built in order to describe the influence degree of sensitive attribute and Quasi-identifier (QI) attributes. We use generalization and BK(L,K)-clustering to present equivalent class, L makes sensitive attributes diversity in one equivalent class. Experimental results show that UDAK-anonymity algorithm are utility, effective and efficient, and can make anonymous uncertainty data effectively resist background knowledge attack and homogeneity attack.

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

2021 ◽  
Vol 25 (5) ◽  
pp. 1247-1271
Author(s):  
Chuanming Chen ◽  
Wenshi Lin ◽  
Shuanggui Zhang ◽  
Zitong Ye ◽  
Qingying Yu ◽  
...  
Keyword(s):  
Private Information ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Background Knowledge ◽  
Sensitive Information ◽  
Frequent Patterns ◽  
Trajectory Data ◽  
Preservation Method ◽  
Sensitive Attribute ◽  
Theoretical Analyses

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

scholarly journals A novel destination prediction attack and corresponding location privacy protection method in geo-social networks

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668542 ◽  
Author(s):  
Di Xue ◽  
Li-Fa Wu ◽  
Hua-Bo Li ◽  
Zheng Hong ◽  
Zhen-Ji Zhou
Keyword(s):  
Social Networks ◽  
Privacy Protection ◽  
Location Privacy ◽  
Background Information ◽  
Data Set ◽  
Attack Model ◽  
Protection Method ◽  
Inference Attacks ◽  
Location Privacy Protection ◽  
Prediction Approach

Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the “data sparsity problem” faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction–based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction + Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction + Check-in Deletion framework.

scholarly journals A Privacy Protection Model for Patient Data With Multiple Sensitive Attributes

2010 ◽  
pp. 44-60
Author(s):  
Tamas S. Gal ◽  
Zhiyuan Chen ◽  
Aryya Gangopadhyay
Keyword(s):  
Privacy Protection ◽  
Practical Method ◽  
Patient Data ◽  
Experimental Results ◽  
Diagnosis And Treatment ◽  
Data Sets ◽  
New Model ◽  
Sensitive Attribute

The identity of patients must be protected when patient data is shared. The two most commonly used models to protect identity of patients are L-diversity and K-anonymity. However, existing work mainly considers data sets with a single sensitive attribute, while patient data often contain multiple sensitive attributes (e.g., diagnosis and treatment). This chapter shows that although the K-anonymity model can be trivially extended to multiple sensitive attributes, L-diversity model cannot. The reason is that achieving L-diversity for each individual sensitive attribute does not guarantee L-diversity over all sensitive attributes. The authors propose a new model that extends L-diversity and K-anonymity to multiple sensitive attributes and propose a practical method to implement this model. Experimental results demonstrate the effectiveness of this approach.

scholarly journals Mineral Element Deposition and Gene Expression across Different Tissues of Cherry Valley Ducks

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 238
Author(s):  
Qianqian Song ◽  
Yi Zhang ◽  
Hao Bai ◽  
Li Zhong ◽  
Xiaofan Li ◽  
...  
Keyword(s):  
Significant Negative Correlation ◽  
Mineral Elements ◽  
Breast Muscle ◽  
Thigh Muscle ◽  
Mrna Levels ◽  
High Quality ◽  
Atomic Fluorescence ◽  
Breeding Efficiency ◽  
Almost All ◽  
Different Tissues

This study was conducted to investigate the deposition of several mineral elements and the mRNA levels of mineral-related genes across different tissues of cherry valley ducks. The contents of magnesium (Mg), potassium (K), zinc (Zn), and selenium (Se) in ducks’ breast muscle, thigh muscle, liver, skin, and tibia at the age of 0, 21, 35, 49, and 63 days, respectively, were measured using an atomic fluorescence spectrophotometer, while the mRNA levels of mineral-related genes were detected by qRT-PCR. The results revealed that the dynamics of Mg and K were generally similar in each tissue, with a significant positive correlation (p < 0.05). In the breast muscle, thigh muscle, and liver, the contents of almost all mineral elements reached their peak values (p < 0.05) at the age of 49 to 63 days. Interestingly, the expression of most mineral-related genes was the highest at birth (p < 0.05). In addition, there was a significant negative correlation between the expression of ATP1A1 and the deposition of K (r = −0.957, p < 0.05), and a similar result was found for the expression of ATP8 and the deposition of Zn (r = −0.905, p < 0.05). Taken together, Mg and K could be used as joint indicators for the precise breeding of the high-quality strain of cherry valley ducks, while the age of 49 to 63 days could be used as the reference for the best marketing age. In addition, ATP1A1 and ATP8 could be used as the key genes to detect K and Zn, respectively. Hence, the findings of this study can be used to improve the production and breeding efficiency of high-quality meat ducks.

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