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Provable Privacy Guarantee and Smart Navigation Attachment to Aid

Enabling Advanced Queries on Untrusted Databases with Provable Privacy Guarantee and Superior Efficiency

Privacy is a fundamental human right. Service providers can track our queries and interests when we search for (or consume) any information on social media, video-streaming sites, public databases, etc. They can infer personally identifiable and sensitive information like political preference, sexual orientation, etc., which users may not like to share. Private information retrieval (PIR) is a powerful cryptographic primitive that solves this ubiquitous problem of safeguarding the privacy of users' access patterns to remote, untrusted databases. Most PIR techniques only support position-based queries - which require the knowledge of the data record or block index - to access the databases. However, in the real world, users are more interested in retrieving records by various search criteria, e.g., searching by keywords, sorting and TopK search, aggregate queries, and ultimately, SQL-like utility. To protect users' sensitive access patterns on untrusted databases and support regular and advanced queries, e.g., aggregate and SQL queries, we need new PIR techniques that provide provable security guarantee and efficiency in terms of computation and communication complexity associated with PIR tasks.

Speaker: Syed Hafiz, UC Davis

Novel Machine Learning-Based Smart Navigation Attachment to Aid Glaucoma Patients

Glaucoma, a serious eye condition, is the second leading cause of blindness in the world, and currently affects 80 million people. Its irreversibility necessitates the use of navigation aids, which are becoming more tech-oriented. However, current smart mobility aids are very costly and are only utilized by 2-6% of the visually impaired population.

We analyzed various existing mobility aids and created a list of successful and necessary components. Our engineering goal is to create a product that mounts onto any size-diameter white cane, detects and recognizes any object, quickly and alerts the user through speakers. With a list of many features, while maintaining a relatively low cost of under $250. Our average accuracy overall was 80.4%. After iterating our design, we calculated our final overall accuracy as 90.0%. We successfully met our criteria of minimum 85% overall accuracy. Our product can help a wider range of visually impaired people navigate the outdoors more safely, efficiently, and cost-effectively.
Video Demo: https://youtu.be/aQUawlxM2vo

Speakers: Reeva Patel, Gauri Todur, Nidhi Thankasala, Cabrillo Middle School Stem Leadership Institute

Stream on YouTube.  See weblink for connection information

Wednesday, 07/20/22


Website: Click to Visit



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SF Bay Association of Computing Machinery

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