Andere Kunden interessierten sich auch für
![A Hybrid Approach for Collaborative Privacy Preserving Data Integration and Publishing]( "A Hybrid Approach for Collaborative Privacy Preserving Data Integration and Publishing")
A Hybrid Approach for Collaborative Privacy Preserving Data Integration and Publishing27,99 €![Privacy Preserving Mechanisms for Databases Development]( "Privacy Preserving Mechanisms for Databases Development")
Privacy Preserving Mechanisms for Databases Development29,99 €![Healthcare Information Security and Privacy]( "Healthcare Information Security and Privacy")
Healthcare Information Security and Privacy59,99 €![Data Quality Assessment]( "Data Quality Assessment")
Data Quality Assessment45,99 €![A Review of Public Data About Terrorism and Targeted Violence to Meet U.S. Department of Homeland Security Mission Needs]( "A Review of Public Data About Terrorism and Targeted Violence to Meet U.S. Department of Homeland Security Mission Needs")
A Review of Public Data About Terrorism and Targeted Violence to Meet U.S. Department of Homeland Security Mission Needs27,99 €![Data Interoperability Community of Interest Handbook]( "Data Interoperability Community of Interest Handbook")
Data Interoperability Community of Interest Handbook65,99 €![Getting Started with Impala]( "Getting Started with Impala")
Getting Started with Impala31,99 €
The rapid growth in the usage of location-based services has resulted in extensive research on users' trajectory data publishing or on continuous publications of location statistics. The users' trajectory information or location statistics are provided valuable knowledge that supports various social benefits such as smart healthcare, real-time traffic monitoring, online advertisement, etc. Even publishing or sharing such users' trajectory or location statistics without preserving users' privacy. Then the participated users in a published dataset may presume that a malicious adversary can breach participated users' privacy because it contains users' private information like disease, habits, etc. In literature, there exist privacy preservation models to provide a privacy guarantee to users against an efficient adversary, namely Data anonymization, -Differential Privacy (-DP), and -Local Differential Privacy (-LDP). Data anonymization model protects users' sensitive information from record/identity linkage, attribute linkage, and table linkage attack, whereas -Differential Privacy, and -Local Differential Privacy address probabilistic attack. The above privacy preserving models preserve users privacy by assuming that data records are independent from each other. However, in reality data records are not independent (or correlated) which leads to achieve less privacy guarantee as compared to traditional privacy preserving models. In other words, if an adversary has additional knowledge about the correlated records, then these privacy models does not prevent all participated users privacy. Specifically, single Data Anonymization approach is not addressed the correlatedrecords linkage attack along with three common linkage attacks, namely identity linkage, attribute linkage, and similarity attack. Further, the traditional DP and LDP mechanisms are not provide sufficient privacy guarantee especially the the data records are in correlated in nature