Background Use of Sensitive Information to Aid in Analysis of Non-sensitive Data on Threats and Vulnerabilities

2005 ◽  
pp. 652-653
Author(s):  
Richard. A. Smith
Keyword(s):  
Sensitive Information ◽  
Sensitive Data

scholarly journals Accurate Filtering of Privacy-Sensitive Information in Raw Genomic Data

2018 ◽  
Author(s):  
Jérémie Decouchant ◽  
Maria Fernandes ◽  
Marcus Völp ◽  
Francisco M Couto ◽  
Paulo Esteves-Veríssimo
Keyword(s):  
High Performance ◽  
Genomic Data ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Variable Regions ◽  
Fine Grained ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
Human Genomes ◽  
Long Reads

AbstractSequencing thousands of human genomes has enabled breakthroughs in many areas, among them precision medicine, the study of rare diseases, and forensics. However, mass collection of such sensitive data entails enormous risks if not protected to the highest standards. In this article, we follow the position and argue that post-alignment privacy is not enough and that data should be automatically protected as early as possible in the genomics workflow, ideally immediately after the data is produced. We show that a previous approach for filtering short reads cannot extend to long reads and present a novel filtering approach that classifies raw genomic data (i.e., whose location and content is not yet determined) into privacy-sensitive (i.e., more affected by a successful privacy attack) and non-privacy-sensitive information. Such a classification allows the fine-grained and automated adjustment of protective measures to mitigate the possible consequences of exposure, in particular when relying on public clouds. We present the first filter that can be indistinctly applied to reads of any length, i.e., making it usable with any recent or future sequencing technologies. The filter is accurate, in the sense that it detects all known sensitive nucleotides except those located in highly variable regions (less than 10 nucleotides remain undetected per genome instead of 100,000 in previous works). It has far less false positives than previously known methods (10% instead of 60%) and can detect sensitive nucleotides despite sequencing errors (86% detected instead of 56% with 2% of mutations). Finally, practical experiments demonstrate high performance, both in terms of throughput and memory consumption.

scholarly journals Enhancing GDPR compliance through data sensitivity and data hiding tools

2021 ◽  
Vol 27 (7) ◽  
pp. 650-666
Author(s):  
Xabier Larrucea ◽  
Micha Moffie ◽  
Dan Mor
Keyword(s):  
Health Services ◽  
Data Hiding ◽  
Industry 4.0 ◽  
Health Sector ◽  
Healthcare Sector ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Architectural Model ◽  
Data Sensitivity

Since the emergence of GDPR, several industries and sectors are setting informatics solutions for fulfilling these rules. The Health sector is considered a critical sector within the Industry 4.0 because it manages sensitive data, and National Health Services are responsible for managing patients’ data. European NHS are converging to a connected system allowing the exchange of sensitive information cross different countries. This paper defines and implements a set of tools for extending the reference architectural model industry 4.0 for the healthcare sector, which are used for enhancing GDPR compliance. These tools are dealing with data sensitivity and data hiding tools A case study illustrates the use of these tools and how they are integrated with the reference architectural model.

Designing Secure Data Warehouses

2008 ◽  
pp. 679-692
Author(s):  
Rodolfo Villarroel ◽  
Eduardo Fernandez-Medina ◽  
Juan Trujillo ◽  
Mario Piattini
Keyword(s):  
Conceptual Modeling ◽  
Systems Design ◽  
Specific Information ◽  
Sensitive Information ◽  
Data Warehouses ◽  
Personal Beliefs ◽  
Operational Environment ◽  
Sensitive Data ◽  
Secure Systems ◽  
Secure Systems Design

Organizations depend increasingly on information systems, which rely upon databases and data warehouses (DWs), which need increasingly more quality and security. Generally, we have to deal with sensitive information such as the diagnosis made on a patient or even personal beliefs or other sensitive data. Therefore, a final DW solution should consider the final users that can have access to certain specific information. Unfortunately, methodologies that incorporate security are based on an operational environment and not on an analytical one. Therefore, they do not include security into the multidimensional approaches to work with DWs. In this chapter, we present a comparison of six secure-systems design methodologies. Next, an extension of the UML that allows us to specify main security aspects in the multidimensional conceptual modeling is proposed, thereby allowing us to design secure DWs. Finally, we present how the conceptual model can be implemented with Oracle Label Security (OLS10g).

Use of Purpose and Role Based Access Control Mechanisms to Protect Data Within RDBMS

2020 ◽  
Vol 8 (1) ◽  
pp. 82-91
Author(s):  
Suraj Krishna Patil ◽  
Sandipkumar Chandrakant Sagare ◽  
Alankar Shantaram Shelar
Keyword(s):  
Access Control ◽  
Privacy Preservation ◽  
Original Data ◽  
Sensitive Information ◽  
Role Based Access Control ◽  
Control Mechanisms ◽  
Sensitive Data ◽  
Access To Resources ◽  
Key Factor ◽  
Role Based

Privacy is the key factor to handle personal and sensitive data, which in large chunks, is stored by database management systems (DBMS). It provides tools and mechanisms to access and analyze data within it. Privacy preservation converts original data into some unknown form, thus protecting personal and sensitive information. Different access control mechanisms such as discretionary access control, mandatory access control is used in DBMS. However, they hardly consider purpose and role-based access control in DBMS, which incorporates policy specification and enforcement. The role based access control (RBAC) regulates the access to resources based on the roles of individual users. Purpose based access control (PuBAC) regulates the access to resources based on purpose for which data can be accessed. It regulates execution of queries based on purpose. The PuRBAC system uses the policies of both, i.e. PuBAC and RBAC, to enforce within RDBMS.

Designing Secure Data Warehouses

2008 ◽  
pp. 1048-1061
Author(s):  
Rodolfo Villarroel ◽  
Eduardo Fernandez-Medina ◽  
Juan Trujillo ◽  
Mario Piattini
Keyword(s):  
Conceptual Modeling ◽  
Systems Design ◽  
Specific Information ◽  
Sensitive Information ◽  
Data Warehouses ◽  
Personal Beliefs ◽  
Operational Environment ◽  
Sensitive Data ◽  
Secure Systems ◽  
Secure Systems Design

Organizations depend increasingly on information systems, which rely upon databases and data warehouses (DWs), which need increasingly more quality and security. Generally, we have to deal with sensitive information such as the diagnosis made on a patient or even personal beliefs or other sensitive data. Therefore, a final DW solution should consider the final users that can have access to certain specific information. Unfortunately, methodologies that incorporate security are based on an operational environment and not on an analytical one. Therefore, they do not include security into the multidimensional approaches to work with DWs. In this chapter, we present a comparison of six secure-systems design methodologies. Next, an extension of the UML that allows us to specify main security aspects in the multidimensional conceptual modeling is proposed, thereby allowing us to design secure DWs. Finally, we present how the conceptual model can be implemented with Oracle Label Security (OLS10g).

scholarly journals Social Engineering Attacks: A Survey

2019 ◽  
Vol 11 (4) ◽  
pp. 89 ◽  
Author(s):  
Fatima Salahdine ◽  
Naima Kaabouch
Keyword(s):  
Communication Systems ◽  
Social Engineering ◽  
Sensitive Information ◽  
Security Measures ◽  
Information Communication ◽  
Sensitive Data ◽  
Health Records ◽  
The Social ◽  
Human Tendency ◽  
Detection Strategies

The advancements in digital communication technology have made communication between humans more accessible and instant. However, personal and sensitive information may be available online through social networks and online services that lack the security measures to protect this information. Communication systems are vulnerable and can easily be penetrated by malicious users through social engineering attacks. These attacks aim at tricking individuals or enterprises into accomplishing actions that benefit attackers or providing them with sensitive data such as social security number, health records, and passwords. Social engineering is one of the biggest challenges facing network security because it exploits the natural human tendency to trust. This paper provides an in-depth survey about the social engineering attacks, their classifications, detection strategies, and prevention procedures.

scholarly journals Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 327 ◽  
Author(s):  
Subhan Ullah ◽  
Lucio Marcenaro ◽  
Bernhard Rinner
Keyword(s):  
Data Transfer ◽  
Single Step ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Smart Camera ◽  
Sensitive Data ◽  
Smart Cameras ◽  
Iot Applications ◽  
Public Verifiability ◽  
Single Receiver

Smart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups.

scholarly journals PP-VCA: A Privacy-Preserving and Verifiable Combinatorial Auction Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mingwu Zhang ◽  
Bingruolan Zhou
Keyword(s):  
Privacy Preserving ◽  
Network Routing ◽  
Combinatorial Auction ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Winner Determination ◽  
Spectrum Auction ◽  
Auction Price ◽  
Data Volume ◽  
Auction Mechanisms

Combinatorial auctions can be employed in the fields such as spectrum auction, network routing, railroad segment, and energy auction, which allow multiple goods to be sold simultaneously and any combination of goods to be bid and the maximum sum of combinations of bidding prices to be calculated. However, in traditional combinatorial auction mechanisms, data concerning bidders’ price and bundle might reveal sensitive information, such as personal preference and competitive relation since the winner determination problem needs to be resolved in terms of sensitive data as above. In order to solve this issue, this paper exploits a privacy-preserving and verifiable combinatorial auction protocol (PP-VCA) to protect bidders’ privacy and ensure the correct auction price in a secure manner, in which we design a one-way and monotonically increasing function to protect a bidder’s bid to enable the auctioneer to pick out the largest bid without revealing any information about bids. Moreover, we design and employ three subprotocols, namely, privacy-preserving winner determination protocol, privacy-preserving scalar protocol, and privacy-preserving verifiable payment determination protocol, to implement the combinatorial auction with bidder privacy and payment verifiability. The results of comprehensive experimental evaluations indicate that our proposed scheme provides a better efficiency and flexibility to meet different types of data volume in terms of the number of goods and bidders.

scholarly journals Leveraging Mobile Nodes for Preserving Node Privacy in Mobile Crowd Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Chen ◽  
Shengbao Zheng ◽  
Zhengqiu Weng
Keyword(s):  
Mobile Node ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Node Mobility ◽  
Mobile Nodes ◽  
Mobile Crowd Sensing ◽  
Sensitive Data ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Mobile Crowd

Mobile crowd sensing has been a very important paradigm for collecting sensing data from a large number of mobile nodes dispersed over a wide area. Although it provides a powerful means for sensing data collection, mobile nodes are subject to privacy leakage risks since the sensing data from a mobile node may contain sensitive information about the sensor node such as physical locations. Therefore, it is essential for mobile crowd sensing to have a privacy preserving scheme to protect the privacy of mobile nodes. A number of approaches have been proposed for preserving node privacy in mobile crowd sensing. Many of the existing approaches manipulate the sensing data so that attackers could not obtain the privacy-sensitive data. The main drawback of these approaches is that the manipulated data have a lower utility in real-world applications. In this paper, we propose an approach called P3 to preserve the privacy of the mobile nodes in a mobile crowd sensing system, leveraging node mobility. In essence, a mobile node determines a routing path that consists of a sequence of intermediate mobile nodes and then forwards the sensing data along the routing path. By using asymmetric encryptions, it is ensured that a malicious node is not able to determine the source nodes by tracing back along the path. With our approach, upper-layer applications are able to access the original sensing data from mobile nodes, while the privacy of the mobile node is not compromised. Our theoretical analysis shows that the proposed approach achieves a high level of privacy preserving capability. The simulation results also show that the proposed approach incurs only modest overhead.

scholarly journals Facilitating the ethical use of health data for the benefit of society: electronic health records, consent and the duty of easy rescue

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160130 ◽  
Author(s):  
Sebastian Porsdam Mann ◽  
Julian Savulescu ◽  
Barbara J. Sahakian
Keyword(s):  
Informed Consent ◽  
Electronic Health Records ◽  
Data Science ◽  
Real Life ◽  
Health Data ◽  
Sensitive Information ◽  
Minimal Risk ◽  
Sensitive Data ◽  
Health Records ◽  
Electronic Health

Advances in data science allow for sophisticated analysis of increasingly large datasets. In the medical context, large volumes of data collected for healthcare purposes are contained in electronic health records (EHRs). The real-life character and sheer amount of data contained in them make EHRs an attractive resource for public health and biomedical research. However, medical records contain sensitive information that could be misused by third parties. Medical confidentiality and respect for patients' privacy and autonomy protect patient data, barring access to health records unless consent is given by the data subject. This creates a situation in which much of the beneficial records-based research is prevented from being used or is seriously undermined, because the refusal of consent by some patients introduces a systematic deviation, known as selection bias, from a representative sample of the general population, thus distorting research findings. Although research exemptions for the requirement of informed consent exist, they are rarely used in practice due to concerns over liability and a general culture of caution. In this paper, we argue that the problem of research access to sensitive data can be understood as a tension between the medical duties of confidentiality and beneficence. We attempt to show that the requirement of informed consent is not appropriate for all kinds of records-based research by distinguishing studies involving minimal risk from those that feature moderate or greater risks. We argue that the duty of easy rescue—the principle that persons should benefit others when this can be done at no or minimal risk to themselves—grounds the removal of consent requirements for minimally risky records-based research. Drawing on this discussion, we propose a risk-adapted framework for the facilitation of ethical uses of health data for the benefit of society. This article is part of the themed issue ‘The ethical impact of data science’.

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