Realtime Biometric Software 11.6 Free Download
Salomon Thoj
The explosive growth of biometrics use (e.g., in surveillance) poses a persistent challenge to keep biometric data private without sacrificing the apps' functionality. We consider private querying of a real-life biometric scan (e.g., a person's face) against a private biometric database. The querier learns only the label(s) of a matching scan(s) (e.g. a person's name), and the database server learns nothing. We formally define Fuzzy Labeled Private Set Intersection (FLPSI), a primitive computing the intersection of noisy input sets by considering closeness/similarity instead of equality. Our FLPSI protocol's communication is sublinear in database size and is concretely efficient. We implement it and apply it to facial search by integrating with our fine-tuned toolchain that maps face images into Hamming space. We have implemented and extensively tested our system, achieving high performance with concretely small network usage: for a 10K-row database, the query response time over WAN (resp. fast LAN) is 146ms (resp. 47ms), transferring 12.1MB; offline precomputation (with no communication) time is 0.94s. FLPSI scales well: for a 1M-row database, online time is 1.66s (WAN) and 1.46s (fast LAN) with 40.8MB of data transfer in online phase and 37.5s in offline precomputation. This improves the state-of-the-art work (SANNS) by 9-25 (on WAN) and 1.2-4 (on fast LAN). Our false non-matching rate is 0.75% for at most 10 false matches over 1M-row DB, which is comparable to underlying plaintext matching algorithm.
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We are witnessing the confluence of personal biometrics, wearables, smart applications and advanced analytics that will transform healthcare, insurance, nutrition, pharmaceuticals, medical research and retail industries. The opportunities are limitless, but so are the challenges from a data management perspective, since wearables and Smart Clothes will generate a significant volume of very personal data and this data will be shared amongst many entities such as healthcare providers, insurance companies, retailers, government agencies and other industry players.
A leading healthcare insurance provider recently contacted my firm to discuss a new project they wanted to launch. It has introduced an option on its customer portal to enable customers to upload their biometrics and other health related data. The insurer wants to build a "Data Lake" to integrate all the biometric data it collects via the portal with pharmacy transactions, healthcare billing data, customer's clinical health records, insurance product data, industry research data and other pertinent data sets.
Its goal is to apply two sets of advanced analytics - one against the historical data of each customer and the other against a customer population, to gain insights such as health related trends, predict health conditions, population health, outliers, identify patients that can benefit from proactive interventions etc. This analysis will drive the creation of new and innovative insurance products, offer incentives to customers to change behaviour, identify opportunities to reduce costs for customers and the insurer, encourage customers to take preventive measures to improve medical outcomes and potentially identify and prevent fraud.
This ongoing quest to make products more relevant to groups and now individuals stems both from product consumption and consumer engagement. This concept of mass personalization is the latest in a series of moves to make products more relevant, more specific.
This is uncharted territory. The challenge that the insurer has is that all the data it wishes to source into the "Data Lake" resides in silos, isn't very well governed, the quality of some of the third party data is suspect and its data management capability isn't very maturity. Consolidating data into a "Data Lake" is advantageous for the insurer, but it has to address the access control, data security and privacy, data ownership and accountability, master data management, reference data management and other capabilities in the "Data Lake" - in order to reap the benefits. Not doing this during the architecture and design phases, can present serious legal, compliance, reputational and financial risks to the business.
This example shows that new products such as wearables and Smart Clothes are going to generate massive amounts of rich data that can benefit individuals and commercial enterprises. This data can also be used by researchers in the bio-technology, genomics and other fields to enrich their data sets.
Acquiring and processing personal and sensitive data is at the heart of all this. Therefore managing, governing and securing it takes on a very important role. This data will be shared across numerous service providers, commercial entities and government agencies. Ensuring that it isn't misused, is handled with care, isn't accessible by unauthorised persons or entities and can be relied upon to make decisions, is becoming a critical mandate - forcing organisations to mature their data management and analytics capabilities (i.e. Data Governance, Master Data Management, Reference Data Management, Data Quality, Metadata Management, Data Science, etc.).
About Me : I am the Founder and Managing Partner of AlyData. As an entrepreneur, advisor and thought leader in Strategic Data Management - I specialise in Strategic Data Management, Analytics and Change Management. My firm and I help companies improve decision-making and performance, using data. I am currently working on a new book on how organisations can become data-driven and am targeting release in the fourth quarter of 2015.
Why I write : I am passionate about Strategic Data Management - an emerging field that is fast maturing. I write because I want organisations to treat data as a strategic asset - to unleash its power. I do this by sharing my personal insights and experiences with you and connect some dots in the process. I hope you take away a few nuggets of information to use in your personal and professional lives.
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