scholarly journals Protecting Privacy and Security of Genomic Data in i2b2 With Homomorphic Encryption and Differential Privacy

2018 ◽  
pp. 1-1 ◽  
Author(s):  
Jean Louis Raisaro ◽  
Gwangbae Choi ◽  
Sylvain Pradervand ◽  
Raphael Colsenet ◽  
Nathalie Jacquemont ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Genomic Data ◽  
Privacy And Security

Security and Privacy Challenges of Deep Learning

2020 ◽  
pp. 42-64
Author(s):  
J. Andrew Onesimu ◽  
Karthikeyan J. ◽  
D. Samuel Joshua Viswas ◽  
Robin D Sebastian
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Detailed Comparison ◽  
Privacy Preserving ◽  
Research Field ◽  
Security And Privacy ◽  
Security Attacks ◽  
Privacy Breaches ◽  
Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

scholarly journals Genomic Data-Sharing Practices

2019 ◽  
Vol 47 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Angela G. Villanueva ◽  
Robert Cook-Deegan ◽  
Jill O. Robinson ◽  
Amy L. McGuire ◽  
Mary A. Majumder
Keyword(s):  
Data Sharing ◽  
Medical Information ◽  
Public Information ◽  
Genomic Data ◽  
Data Access ◽  
Privacy And Security ◽  
Participant Engagement ◽  
Information Commons ◽  
Derived Data

Making data broadly accessible is essential to creating a medical information commons (MIC). Transparency about data-sharing practices can cultivate trust among prospective and existing MIC participants. We present an analysis of 34 initiatives sharing DNA-derived data based on public information. We describe data-sharing practices captured, including practices related to consent, privacy and security, data access, oversight, and participant engagement. Our results reveal that data-sharing initiatives have some distance to go in achieving transparency.

Ensuring privacy and security of genomic data and functionalities

2019 ◽  
Vol 21 (2) ◽  
pp. 511-526 ◽  
Author(s):  
Abukari Mohammed Yakubu ◽  
Yi-Ping Phoebe Chen
Keyword(s):  
Association Studies ◽  
Diagnostic Testing ◽  
Healthcare Delivery ◽  
Genomic Data ◽  
Security And Privacy ◽  
Genome Wide Association Studies ◽  
Privacy And Security ◽  
Security Issues ◽  
Genomic Privacy ◽  
Privacy Problem

Abstract In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities—computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

scholarly journals Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jinbo Xiong ◽  
Rong Ma ◽  
Lei Chen ◽  
Youliang Tian ◽  
Li Lin ◽  
...  
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Mobile Crowdsensing ◽  
Sensing Data ◽  
Noise Data ◽  
And Performance ◽  
Further Development ◽  
The Internet Of Things

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

scholarly journals An Enhanced Methodology on Internet of Things with Cloud in Smart Electrical Systems

2019 ◽  
Vol 8 (3) ◽  
pp. 2295-2299
Keyword(s):  
Internet Of Things ◽  
Performance Management ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Human Life ◽  
Vital Role ◽  
Electrical System ◽  
Privacy And Security ◽  
Security Issues ◽  
Electrical Systems

The smart management system plays a vital role in many domains and improves the reliability of protection and privacy of a system. Electrical systems have become a part in everyday human life. The next generation electrical systems will entirely depends on fully automated and smart control systems. In the present paper various mechanisms of cloud gateways and security issues are explored for smart management of an electrical system. The present survey work is reconnoitred with Internet of Things (IoT) in association with cloud. Cloud based IoT in smart electrical system provides potential enhancement of performance, management, and resilience of the smart system. However, the espousal of cloud based IoT system in smart electrical system to store and retrieve the data from cloud may increase risks in data privacy and security. Despite the different flaws in global integration of cloud with IoT through internet, various end-to-end security schemes are discussed to overcome these flaws. As a result in many of the applications easy IoT cloud gateway along with homomorphic encryption technique is set up to solve communication overheads and security issues.

scholarly journals Multiparty Reach and Frequency Histogram: Private, Secure, and Practical

2021 ◽  
Vol 2022 (1) ◽  
pp. 373-395
Author(s):  
Badih Ghazi ◽  
Ben Kreuter ◽  
Ravi Kumar ◽  
Pasin Manurangsi ◽  
Jiayu Peng ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Bloom Filter ◽  
Small Error ◽  
Privacy And Security ◽  
Frequency Histogram ◽  
Security Guarantees ◽  
Counting Bloom Filter

Abstract Consider the setting where multiple parties each hold a multiset of users and the task is to estimate the reach (i.e., the number of distinct users appearing across all parties) and the frequency histogram (i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.

scholarly journals Privacy Preserving Outsourced Calculations With Symmetric Fully Homomorphic Encryption

2019 ◽  
Vol 8 (10) ◽  
pp. 3012-3015
Keyword(s):  
Service Providers ◽  
Homomorphic Encryption ◽  
Cloud Service ◽  
New Paradigm ◽  
Fully Homomorphic Encryption ◽  
Privacy And Security ◽  
Cloud Service Provider ◽  
Privacy Concerns ◽  
Private Data ◽  
Cloud Storage Service

Cloud computing is a new paradigm which provides cloud storage service to manage, maintain and back up private data remotely. For privacy concerns the data is kept encrypted and made available to users on demand through cloud service provider over the internet. The legacy encryption techniques rely on sharing of keys, so service providers and end users of the cloud have exclusive rights on the data thus the secrecy may loss. Homomorphic Encryption is a significant encryption technique which allows users to perform limited arithmetic on the enciphered data without loss of privacy and security. This paper addresses a new simple and non-bootstrappable Fully Homomorphic Encryption Scheme based on matrices as symmetric keys with access control.

scholarly journals Motion Detection to Preserve Personal Privacy from Surveillance Data using Contrary Motion

2020 ◽  
Vol 8 (6) ◽  
pp. 3892-3895
Keyword(s):  
Internet Of Things ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Smart Cities ◽  
Value Added ◽  
The Internet ◽  
Sufficient Information ◽  
Personal Privacy ◽  
Privacy And Security ◽  
The Internet Of Things

Internet of Things network today naturally is one of the huge quantities of devices from sensors linked through the communication framework to give value added service to the society and mankind. That allows equipment to be connected at anytime with anything rather using network and service. By 2020 there will be 50 to 100 billion devices connected to Internet and will generate heavy data that is to be analyzed for knowledge mining is a forecast. The data collected from individual devices of IoT is not going to give sufficient information to perform any type of analysis like disaster management, sentiment analysis, and smart cities and on surveillance. Privacy and Security related research increasing from last few years. IoT generated data is very huge, and the existing mechanisms like k- anonymity, l-diversity and differential privacy were not able to address these personal privacy issues because the Internet of Things Era is more vulnerable than the Internet Era [10][20]. To solve the personal privacy related problems researchers and IT professionals have to pay more attention to derive policies and to address the key issues of personal privacy preservation, so the utility and trade off will be increased to the Internet of Things applications. Personal Privacy Preserving Data Publication (PPPDP) is the area where the problems are identified and fixed in this IoT Era to ensure better personal privacy.

scholarly journals Clouded data: Privacy and the promise of encryption

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171984878
Author(s):  
Luke Munn ◽  
Tsvetelina Hristova ◽  
Liam Magee
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Personal Data ◽  
Individual Identification ◽  
Student Survey ◽  
Public And Private ◽  
The Public ◽  
Synthetic Datasets ◽  
Computational Resources

Personal data is highly vulnerable to security exploits, spurring moves to lock it down through encryption, to cryptographically ‘cloud’ it. But personal data is also highly valuable to corporations and states, triggering moves to unlock its insights by relocating it in the cloud. We characterise this twinned condition as ‘clouded data’. Clouded data constructs a political and technological notion of privacy that operates through the intersection of corporate power, computational resources and the ability to obfuscate, gain insights from and valorise a dependency between public and private. First, we survey prominent clouded data approaches (blockchain, multiparty computation, differential privacy, and homomorphic encryption), suggesting their particular affordances produce distinctive versions of privacy. Next, we perform two notional code-based experiments using synthetic datasets. In the field of health, we submit a patient’s blood pressure to a notional cloud-based diagnostics service; in education, we construct a student survey that enables aggregate reporting without individual identification. We argue that these technical affordances legitimate new political claims to capture and commodify personal data. The final section broadens the discussion to consider the political force of clouded data and its reconstitution of traditional notions such as the public and the private.

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