PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Searchable symmetric encryption (SSE) has been used to protect the confidentiality of genomic data while providing substring search and range queries on a sequence of genomic data, but it has not been studied for protecting single nucleotide polymorphism (SNP)-phenotype data. In this article, we propose a novel model, PrivGenDB, for securely storing and efficiently conducting different queries on genomic data outsourced to an honest-but-curious cloud server. To instantiate PrivGenDB, we use SSE to ensure confidentiality while conducting different types of queries on encrypted genomic data, phenotype and other information of individuals to help analysts/clinicians in their analysis/care. To the best of our knowledge, PrivGenDB construction is the first SSE-based approach ensuring the confidentiality of shared SNP-phenotype data through encryption while making the computation/query process efficient and scalable for biomedical research and care. Furthermore, it supports a variety of query types on genomic data, including count queries, Boolean queries, and k'-out-of-k match queries. Finally, the PrivGenDB model handles the dataset containing both genotype and phenotype, and it also supports storing and managing other metadata like gender and ethnicity privately. Computer evaluations on a dataset with 5,000 records and 1,000 SNPs demonstrate that a count/Boolean query and a k'-out-of-k match query over 40 SNPs take approximately 4.3s and 86.4{\mu}s, respectively, that outperforms the existing schemes

Quantum surveillance and 'shared secrets'. A biometric step too far? CEPS Liberty and Security in Europe, July 2010

It is no longer sensible to regard biometrics as having neutral socio-economic, legal and political impacts. Newer generation biometrics are fluid and include behavioural and emotional data that can be combined with other data. Therefore, a range of issues needs to be reviewed in light of the increasing privatisation of ‘security’ that escapes effective, democratic parliamentary and regulatory control and oversight at national, international and EU levels, argues Juliet Lodge, Professor and co-Director of the Jean Monnet European Centre of Excellence at the University of Leeds, U

Privacy Violation and Detection Using Pattern Mining Techniques

Privacy, its violations and techniques to bypass privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a Privacy Violation Detection and Monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. Experimental evaluations show that our approach is scalable and robust and that it can detect privacy violations or chances of violations quite accurately. Please contact the author for full text at [email protected]

Personal Privacy Protection within Pervasive RFID Environments

Recent advancements in location tracking technologies have increased the threat to an individual\u27s personal privacy. Radio frequency identification (RFID) technology allows for the identification and potentially continuous tracking of an object or individual, without obtaining the individual\u27s consent or even awareness that the tracking is taking place. Although many positive applications for RFID technology exist, for example in the commercial sector and law enforcement, the potential for abuse in the collection and use of personal information through this technology also exists. Location data linked to other types of personal information allows not only the detection of past spatial travel and activity patterns, but also inferences regarding past and future behavior and preferences. Legislative and technological solutions to deal with the increased privacy threat raised by this and similar tracking technologies have been proposed. Such approaches in isolation have significant limitations. This thesis hypothesizes that an approach may be developed with high potential for sufficiently protecting individual privacy in the use of RFID technologies while also strongly supporting marketplace uses of such tags. The research develops and investigates the limits of approaches that might be us,ed to protect privacy in pervasive RFID surveillance environments. The conclusion is ultimately reached that an approach facilitating individual control over the linking of unique RFID tag ID numbers to personal identity implemented though a combination of legal controls and technological capabilities would be a highly desirable option in balancing the interests of both the commercial sector and the information privacy interests of individuals. The specific model developed is responsive to the core ethical principle of autonomy of the individual and as such is also intended to be more responsive to the needs of individual consumers. The technological approach proposed integrated with enabling privacy legislation and private contract law to enable interactive alteration of privacy preferences should result in marketplace solutions acceptable to both potential commercial users and those being tracked

Personal Privacy Protection within Pervasive RFID Environments

Recent advancements in location tracking technologies have increased the threat to an individual\u27s personal privacy. Radio frequency identification (RFID) technology allows for the identification and potentially continuous tracking of an object or individual, without obtaining the individual\u27s consent or even awareness that the tracking is taking place. Although many positive applications for RFID technology exist, for example in the commercial sector and law enforcement, the potential for abuse in the collection and use of personal information through this technology also exists. Location data linked to other types of personal information allows not only the detection of past spatial travel and activity patterns, but also inferences regarding past and future behavior and preferences. Legislative and technological solutions to deal with the increased privacy threat raised by this and similar tracking technologies have been proposed. Such approaches in isolation have significant limitations. This thesis hypothesizes that an approach may be developed with high potential for sufficiently protecting individual privacy in the use of RFID technologies while also strongly supporting marketplace uses of such tags. The research develops and investigates the limits of approaches that might be us,ed to protect privacy in pervasive RFID surveillance environments. The conclusion is ultimately reached that an approach facilitating individual control over the linking of unique RFID tag ID numbers to personal identity implemented though a combination of legal controls and technological capabilities would be a highly desirable option in balancing the interests of both the commercial sector and the information privacy interests of individuals. The specific model developed is responsive to the core ethical principle of autonomy of the individual and as such is also intended to be more responsive to the needs of individual consumers. The technological approach proposed integrated with enabling privacy legislation and private contract law to enable interactive alteration of privacy preferences should result in marketplace solutions acceptable to both potential commercial users and those being tracked

Location Privacy in the Era of Big Data and Machine Learning

Location data of individuals is one of the most sensitive sources of information that once revealed to ill-intended individuals or service providers, can cause severe privacy concerns. In this thesis, we aim at preserving the privacy of users in telecommunication networks against untrusted service providers as well as improving their privacy in the publication of location datasets. For improving the location privacy of users in telecommunication networks, we consider the movement of users in trajectories and investigate the threats that the query history may pose on location privacy. We develop an attack model based on the Viterbi algorithm termed as Viterbi attack, which represents a realistic privacy threat in trajectories. Next, we propose a metric called transition entropy that helps to evaluate the performance of dummy generation algorithms, followed by developing a robust dummy generation algorithm that can defend users against the Viterbi attack. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. For privacy preserving data publishing, an enhanced framework for anonymization of spatio-temporal trajectory datasets termed the machine learning based anonymization (MLA) is proposed. The framework consists of a robust alignment technique and a machine learning approach for clustering datasets. The framework and all the proposed algorithms are applied to the Geolife dataset, which includes GPS logs of over 180 users in Beijing, China

CYBERSECURITY IN THE HEALTHCARE ENVIRONMENT

Abstract In 1990 the online world began to take shape when Tim Berners-Lee invented the World Wide Web. Almost simultaneously, cybersecurity was birthed to protect and minimize the various threats including but not limited to worms, viruses, and data breaches. Cybersecurity includes the various technologies, equipment both hardware and software, processes, and procedures that are used to guard against unauthorized attacks or access to protected information. This paper will focus on cybersecurity as it relates to the healthcare environment. Every department in a healthcare facility is responsible for taking care of patients. This should be their number one priority and information technology is no exception. While IT staff most likely do not provide hands on care to patients, they go to great lengths to protect their personal health information. In a healthcare environment, there are numerous departments such as Lab, Radiology, and Pharmacy etc. that need to have integrated systems. These systems must also be able to reach the internet and often be accessible to outside/non-employed vendors for support and maintenance. Also, communication among employees and with the outside world is a must. Email, video conferencing, desktop sharing, and faxing are all used thousands of times a day. It is imperative that cybersecurity be a top priority and everyone holds himself or herself responsible for protecting the systems that allow staff to take care of their patients