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B2B marketing platform Integrate has announced the release of a new solution designed to ensure that lead data remains compliant with privacy regulations. Data Guardian uses AI-enhanced trust scoring and auditing to protect privacy as well as to increase visibility into media partner performance.

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Background: The utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution's protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape.

Results: We show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation.

Conclusion: The results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

Sharing health and social care data is essential to the delivery of high quality health care as well as disease surveillance, public health, and for conducting research. However, these societal benefits may be constrained by privacy and data protection principles. Hence, societies are striving to find a balance between the two competing public interests. Whilst the spread of IT advancements in recent decades has increased the demand for an increased privacy and data protection in many ways health is a special case. UK are adopting guidelines, codes of conduct and regulatory instruments aimed to implement privacy principles into practical settings and enhance public trust. Accordingly, in 2015, the UK National Data Guardian (NDG) requested to conduct a further review of data protection, referred to as Caldicott 3. The scope of this review is to strengthen data security standards and confidentiality. It also proposes a consent system based on an "opt-out" model rather than on "opt-in.Across Europe as well as internationally the privacy-health data sharing balance is not fixed. In Europe enactment of the new EU Data Protection Regulation in 2016 constitute a major breakthrough, which is likely to have a profound effect on European countries and beyond. In Australia and across North America different ways are being sought to balance out these twin requirements of a modern society - to preserve privacy alongside affording high quality health care for an ageing population. Whilst in the UK privacy legal framework remains complex and fragmented into different layers of legislation, which may negatively impact on both the rights to privacy and health the UK is at the forefront in the uptake of international and EU privacy and data protection principles. And, if the privacy regime were reorganised in a more comprehensive manner, it could be used as a sound implementation model for other countries.

Integrate, a precision demand marketing company, released Data Guardian, a data protection offering designed to help ensure marketing leads are high quality and compliant in the highly regulated data landscape.

This paper begins by providing a background on some of the strategies that have been adopted to preserve spatial data confidentiality with a particular focus on geomasking. Next, we discuss the mathematical formulation of point data transformations and re-transformations, and the workflow and technical implementation for Privy using some analytical and statistical experiments for illustration. Finally, the paper discusses some of the limitations and shortcomings of Privy along with a future direction for this type of spatial data confidentiality research.

Privacy policies define restrictions for the release of individual location data to third parties [28]. For example, the Health Insurance Portability and Accountability Act (HIPPA) requires health data that are visualized by zip code should have a denominator population of at least 20,000. Besides federal laws such as HIPPA, there are human subject protection procedures implemented by IRBs. Even though IRBs review and monitor the collection and use of personally identifiable information, uncertainty still exists within these bodies regarding what are acceptable risks of disclosure with respect to maps and other spatial outputs [10].

Among all spatial privacy-preserving methodologies the most commonly used and studied is spatial data obfuscation or geomasking. Obfuscation can be considered as a combination of statistical and epidemiological techniques to mask location information in a way that can still enable meaningful analysis [7, 25, 52]. The two main goals of spatial data obfuscation are to achieve a balance between personal location information protection, and to extract maximum information from fine scale spatial data [25]. Unfortunately, these two goals are inversely related, i.e. the finer the spatial location involved (often preferred for intervention-style analysis), the greater the risk of re-engineering [36]. Many obfuscation methods such as geomasks [1, 7, 25, 30, 45, 51, 54], grid masks [23], and software agents [32] have been suggested to achieve a balance between confidentiality and data utility.

The recent developments in Artificial Intelligence (AI), Internet of Things (IoT), and blockchain have spurred a new wave of interest among researchers to develop novel approaches for preserving confidentiality (both spatial and aspatial). As an example, blockchain technology, which uses encryption and data storage in a decentralized and distributed fashion could be an ideal framework for sharing health data [33]. Apart from storing data in a secure way using encryption, blockchain can be used to create instructions on data ownership and data access (smart contracts [38]) which is particularly useful for tasks such as health supply chain management, data sharing, and consent for clinical trials [33]. One of the recent developments in the area of geospatially-enabled block-chain, FOAM [2], utilizes a crypto-spatial coordinate system for preserving geo-spatial data. FOAM blockchain, apart from validating specific time of an entry, validates the associated proof of location for the entry. Geospatial cryptography [31], which is similar to crypto-spatial coordinate system, utilizes homomorphic cryptography which is defined as a procedure that encrypts data in such a fashion that mathematical operations can be performed on the data without decryption, to securely transfer and analyze geospatial data. Even though nuanced methodologies such as geospatial blockchains are progressing consistently, some of the challenges associated with it such as interoperability, blockchain security, and transparency, still require further attention before full implementation [33]. Software agents provide another methodology for geospatial privacy preservation. This approach is based on controlling access to original individual records without releasing personally identifiable details [32]. Apart from ameliorating the deficiencies presented by releasing spatially aggregated data, the risk of re-identification is much lower with software agents when compared to geo-masked data. Though very promising, the use of software agents to handle confidential health datasets is still at its infancy due to the challenges related to establishing highly secure computer infrastructure. The recent advances in cyberinfrastructure offer promise in the revamping of software agents, though yet again, these methods do not offer immediate solutions to a health care organization requiring spatial expertise now.

Obfuscation by point data translation and rotation. An offset generated from a random number is used for the translation and the rotation is performed using a rotation matrix. The grey dot indicates a point that has been transformed in space

The re-transformation procedure utilizes the random number saved to the local database. First, an X-degree anti-clockwise re-rotation occurs which essentially brings the transformed coordinates into the same orientation as that of the real data. Then the user-supplied key is utilized to retrieve the random number used for the translation, resulting in all coordinates being re-transformed to the original location (Fig. 2) (Eq. (2)).

Re-transformation of obfuscated point data through rotation and re-translation. The point data are rotated in space using the rotation matrix and re-translation is performed using the offset generated from the random number. The grey dot indicates a point that has been re-transformed in space be457b7860