Aggregate Searchable Encryption With Result Privacy

2021 ◽  
pp. 922-945
Author(s):  
Dhruti P. Sharma ◽  
Devesh C. Jinwala
Keyword(s):  
Data Analysis ◽  
Count Data ◽  
Data Extraction ◽  
Searchable Encryption ◽  
Primary Concern ◽  
Conjunctive Query ◽  
Encrypted Data ◽  
Storage Server ◽  
Partial Data ◽  
Aggregate Query

With searchable encryption (SE), the user is allowed to extract partial data from stored ciphertexts from the storage server, based on a chosen query of keywords. A majority of the existing SE schemes support SQL search query, i.e. 'Select * where (list of keywords).' However, applications for encrypted data analysis often need to count data matched with a query, instead of data extraction. For such applications, the execution of SQL aggregate query, i.e. 'Count * where (list of keywords)' at server is essential. Additionally, in case of semi-honest server, privacy of aggregate result is of primary concern. In this article, the authors propose an aggregate searchable encryption with result privacy (ASE-RP) that includes ASearch() algorithm. The proposed ASearch() performs aggregate operation (i.e. Count *) on the implicitly searched ciphertexts (for the conjunctive query) and outputs an encrypted result. The server, due to encrypted form of aggregate result, would not be able to get actual count unless having a decryption key and hence ASearch() offers result privacy.

Aggregate Searchable Encryption With Result Privacy

2020 ◽  
Vol 14 (2) ◽  
pp. 62-82
Author(s):  
Dhruti P. Sharma ◽  
Devesh C. Jinwala
Keyword(s):  
Count Data ◽  
Data Extraction ◽  
Searchable Encryption ◽  
Primary Concern ◽  
Conjunctive Query ◽  
Search Query ◽  
Encrypted Data ◽  
Storage Server ◽  
Partial Data ◽  
Aggregate Query

With searchable encryption (SE), the user is allowed to extract partial data from stored ciphertexts from the storage server, based on a chosen query of keywords. A majority of the existing SE schemes support SQL search query, i.e. 'Select * where (list of keywords).' However, applications for encrypted data analysis often need to count data matched with a query, instead of data extraction. For such applications, the execution of SQL aggregate query, i.e. 'Count * where (list of keywords)' at server is essential. Additionally, in case of semi-honest server, privacy of aggregate result is of primary concern. In this article, the authors propose an aggregate searchable encryption with result privacy (ASE-RP) that includes ASearch() algorithm. The proposed ASearch() performs aggregate operation (i.e. Count *) on the implicitly searched ciphertexts (for the conjunctive query) and outputs an encrypted result. The server, due to encrypted form of aggregate result, would not be able to get actual count unless having a decryption key and hence ASearch() offers result privacy.

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

Expanding Organizational Research Methods

2018 ◽  
pp. 168-183
Author(s):  
Sarah Jane Blithe ◽  
Anna Wiederhold Wolfe
Keyword(s):  
Data Analysis ◽  
Research Methods ◽  
Sex Workers ◽  
Qualitative Data ◽  
Interview Data ◽  
Organizational Research ◽  
Partial Data ◽  
Interview Performance ◽  
Temporal Shifts

Collecting qualitative data in organizations is a complex and messy process which produces subjective, performed, and partial data. In this chapter, the authors argue that analyzing “ruptures” in organizational interview data—paying attention to absences, exits, unspoken feelings, and temporal shifts--can enrich the researcher's understanding by making visible multiple aspects of the data which might otherwise have been overlooked. Examining ruptures draws attention to jarring disjunctures and previously unseen angles often missed through traditional data analysis. Drawing from interview data with brothel owners and sex workers in Nevada's legal brothels, the authors present two main contributions to qualitative organizational research: (1) the benefits of analyzing ruptures in organizational interview performances and transcripts and (2) a challenge to organizational researchers to take seriously their emotions during the interview performance.

Statistical analysis of the item-count technique using Stata

2019 ◽  
Vol 19 (2) ◽  
pp. 390-434
Author(s):  
Chi-lin Tsai
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Count Data ◽  
Profile Likelihood ◽  
Likelihood Estimation ◽  
Great Flexibility ◽  
Recent Developments ◽  
List Experiment ◽  
Experiment Technique ◽  
Item Count Technique

In this article, I review recent developments of the item-count technique (also known as the unmatched-count or list-experiment technique) and introduce a new package, kict, for statistical analysis of the item-count data. This package contains four commands: kict deff performs a diagnostic test to detect the violation of an assumption underlying the item-count technique. kict ls and kict ml perform least-squares estimation and maximum likelihood estimation, respectively. Each encompasses a number of estimators, offering great flexibility for data analysis. kict pfci is a postestimation command for producing confidence intervals with better coverage based on profile likelihood. The development of the item-count technique is still ongoing. I will continue to update the kict package accordingly.

scholarly journals Application of Technological Solutions in the Fight against Money Laundering—A Systematic Literature Review

2019 ◽  
Vol 9 (22) ◽  
pp. 4800 ◽  
Author(s):  
Leite ◽  
Albuquerque ◽  
Pinheiro
Keyword(s):  
Data Analysis ◽  
Money Laundering ◽  
Data Extraction ◽  
Convincing Evidence ◽  
Review Of The Literature ◽  
Exclusion Criteria ◽  
Analysis And Synthesis ◽  
Support Mechanisms ◽  
Combat Money Laundering

With the growing interest in technological solutions aimed at combating money laundering, several studies involving the application of technology have been carried out. However, there were no records of studies aimed at identifying, selecting, rigorously analyzing and synthesizing the literature on solutions that adopt technology to combat money laundering. This paper presents a systematic review of the literature on the application of technological solutions in the fight against money laundering. Seventy-one papers were selected from the 795 studies initially retrieved for data extraction, analysis and synthesis based on predefined inclusion and exclusion criteria. The results obtained with the data analysis made it possible to identify a general categorization of the domains of application of the approaches, as well as a mapping and classification of the support mechanisms adopted. The findings of this review showed that, among the application domain categories identified, the detection of suspicious transactions attracted greater attention from researchers. Regarding the support mechanisms adopted, the application of data mining techniques was used more extensively to detect money laundering. Topics for further research and refinement were also identified, such as the need for a better description of data analysis to provide more convincing evidence to support the benefits presented.

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