Private Information Retrieval for Large-Scale DNA Based Data Storage @SNIAVideo

Uploaded August 2026 | Updated September 2026, 2 weeks ago

Synthetic DNA-based data storage is a promising medium for ultra-dense, long-term archival. Private Information Retrieval (PIR) allows a user to retrieve a specific record from a database without revealing which record is accessed. However, classical PIR protocols assume digital storage with efficient random access and in-memory algebraic computation. Extending PIR to DNA-based data storage presents unique challenges due to biochemical query mechanisms and the potential massive scale of DNA datasets. Let n represent the total number of records in the database. A direct implementation of classical PIR would require an O(n) targeted synthesis effort per query, which is prohibitively expensive. Furthermore, DNA-based data storage lacks support for pre-sequencing in-pool algebraic computation.

Presented by

Zohar Yakhini, Technion

This is a presentation from the 2026 Storage and Computing with DNA Conference.
· Learn More about the SNIA DNA Data Storage Alliance: snia.org/groups/snia-dna-technology-affiliate
· SNIA Educational Library: snia.org/library
· X: twitter.com/SNIA
· LinkedIn: linkedin.com/company/snia

Private Information Retrieval for Large-Scale DNA Based Data Storage

SNIA SDC 2025 - War Stories from the Storage Trenches

How Agentic AI Transforms the Role of Storage

Q&A Podcast for From Data to Decisions: Understanding How AI Models Learn

SNIA Storage Management Community - 2026 SNIA Preview

SNIA SDC: StorageAI 2026 - AI Server Clusters Networking and Storage

SNIA Partner Update - Welcome -June 2024

$Deep Learning Guided DNA DNA Binding Prediction for Scalable DNA Data Storage Motivation: DNA is a promising solution for the global data explosion, yet DNA data storage design remains constrained by the need for sequence orthogonality. Weak, non-specific interactions are often avoided primarily due to lack of models that can accurately and rapidly predict such interactions and lack of large-scale experimental datasets to train such models. This limitation significantly restricts the usable molecular sequence space. Objective: We address these challenges by generating a massive wet lab dataset capturing a broad interaction space and developing a deep learning classifier, BINND, to predict DNA-DNA binding. Method: To build the dataset, a library of randomized 20-mer overhang sequences was annealed to 26 different biotinylated bead primer sequences. Using streptavidin magnetic beads, the bound library strands were separated from the unbound fraction. Post-sequencing and preprocessing, we generated a dataset comprising 144 million 20-mer DNA sequence pairs. BINND is a Convolutional Neural Network classifier trained and evaluated on this large-scale wet lab data. We benchmarked BINND against popular binding predictors such as Gibbs free energy difference, Levenshtein distance, and Hamming distance, focusing on correctness, execution time and memory consumption. We also developed BINND-Lite, a compact version optimized for faster training and lower computational overhead with minimal impact on accuracy. Key results: BINND archives an accuracy of 84%, outperforming the state of the art thermodynamic models by 10% while operating 50x faster. The model generalizes effectively across diverse sequence spaces and varied interaction environments. To demonstrate practical application, we leveraged non-specific interactions to create a physically searchable DNA database representing fictitious children’s book characters and their attributes, enabling fuzzy search capabilities. Conclusion: BINND serves as the first architectural benchmark for DNA-DNA interaction at the 100-million+ sample scale. Presented by Gunavaran Brihadiswaran, North Carolina State University This is a presentation from the 2026 Storage and Computing with DNA Conference. · Learn More about the SNIA DNA Data Storage Alliance: https://www.snia.org/groups/snia-dna-technology-affiliate · SNIA Educational Library: https://snia.org/library · X: https://twitter.com/SNIA · LinkedIn: https://linkedin.com/company/snia/$

SNIA SDC 2025 - Blending Objects & Files in Google Cloud Storage

SNIA SDC 2025 - Disaggregated KV Storage: A New Tier for Efficient Scalable LLM Inference

SNIA SDC 2025 - CSAL w/ Core Scaling for RAID5F: Revolutionizing Cloud Storage Perf & Reliability

Short Video: SAS Continues to Innovate with SBC-5