A cryptography based privacy preserving solution to mine cloud data

Singh, Munindar P.; Krishna, P. Radha; Saxena, Ashutosh

doi:10.1145/1754288.1754302

Cited by 25 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step (iii): location of each observation's k − 1 nearest neighbours This step of the method locates the k −1 nearest neighbours of each observation, for which we use the so-called k-NN (k nearest neighbours) algorithm. This procedure is based on a technique in machine learning and is widely used in several disciplines such as computational geometry [21], diagnostic medicine [22], cryptography [23], data mining [24], and as here, in solutions for data privacy [25].…”

Section: Anonymisation Proceduresmentioning

confidence: 99%

A Deterministic Approach for Protecting Privacy in Sensitive Personal Data

Avraam

Jones

Burton

2021

Preprint

View full text Add to dashboard Cite

Background: Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. However, no single solution can fully protect a dataset and often combinations of methods and multifaceted systems are essential.Results: We propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables preserving their spatial properties.Conclusions: The procedure makes data re-identification difficult while minimising the loss of utility; the latter means that informative statistical analysis can still be conducted. We demonstrate its use on real data, including data from the 1958 Birth Cohort.

show abstract

Section: Anonymisation Proceduresmentioning

confidence: 99%

A Deterministic Approach for Protecting Privacy in Sensitive Personal Data

Avraam

Jones

Burton

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The fact that personal data are examined is considered as a privacy violation if it's not done properly. This kind of procedures should provide basic anonymization through the data analysis in order for the client's privacy to be ensured [19].…”

Section: Privacy Preserving Data Miningmentioning

confidence: 99%

Privacy in the Cloud: Bridging the Gap between Design and Implementation

Manousakis

Kalloniatis

Kavakli

et al. 2013

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Bridging the gap between design and implementation stages has been a major concern that deplores designers, analysts and developers for quite a long time during the design and implementation of information systems in traditional environments. This issue grows to bigger dimension with the presence of cloud computing. Designing and modeling an Information System for the Cloud is a major and hard task that most of the traditional software engineering approaches fail to fulfill. In parallel, many respective organisations and respective researchers have highlighted a number of security and privacy challenges that are not present in traditional environments and need special attention when implementing or migrating information systems into a cloud environment. Thus, security and privacy are by themselves two areas that need special attention in the cloud era. This paper moves on to this direction. Specifically, it presents a number of privacy-oriented technical concepts that analysts need to consider when designing and modeling privacy-aware systems in a cloud environment. Also it suggest for every concept a number of implementation techniques that can assist developers in implementing the respective concepts.

show abstract

“…Ling et al [15] have proposed solution based on bloom filter to protect organizations data repurposed for business intelligence and customer private data. A weighted k-NN classification approach has been suggested by Meena et al [16], for mining data in the cloud environment. Synthetic data generation, which is not the same as original data, has been proposed by Vishal et al [17], especially for organizations who have chosen to outsource data mining tasks, as in KPO.…”

Section: Preserving Data Privacymentioning

confidence: 99%