Trusted CI Webinar: Privacy Preserving Aggregate Range Queries on Encrypted Multi-dimensional Databases, Monday November 11th @10am Central

Augusta University's Hoda Maleki presenting the talk, Privacy Preserving Aggregate Range Queries on Encrypted Multi-dimensional Databases, on November 18th at 10am, Central time.

Please register here.

Data-driven collaborations often involve sharing large-scale datasets in cloud environments, where adversaries may exploit server vulnerabilities to access sensitive information. Traditional approaches, such as Trusted Execution Environments, lack the scalability for parallel processing, while techniques like homomorphic encryption incur prohibitive computational overheads. ARMOR addresses these limitations by developing encrypted querying techniques that support a variety of scientific data types and queries, balancing efficiency with privacy. The project’s interdisciplinary team focuses on advancing encryption methods, improving query performance for multidimensional data, and rigorously evaluating security risks and overheads under real-world scenarios.

A recent research under ARMOR is the development of Secure Standard Aggregate Queries (SSAQ), a novel approach for secure aggregation on multidimensional sparse datasets stored on untrusted servers. Aggregation functions like SUM, AVG, COUNT, MIN, MAX, and STD are essential for scientific data analysis but pose privacy risks when performed on encrypted data. Existing methods using searchable encryption suffer from access pattern and volume leakage and are often limited to one-dimensional settings. SSAQ overcomes these challenges by employing d-dimensional segment trees to precompute responses for all possible query ranges, thus improving the efficiency of secure range queries.

To further reduce leakage, SSAQ integrates Oblivious RAM (ORAM) to conceal data access patterns during query execution. This combination ensures a higher level of security, making SSAQ suitable for complex scientific data scenarios where sensitive information needs to be safeguarded. The approach significantly extends the applicability of searchable encryption techniques, offering a scalable and efficient solution for secure data analytics in cloud environments while minimizing privacy risks.

Speaker Bio: 

Dr. Hoda Maleki is an Assistant Professor in the School of Computer and Cyber Sciences at Augusta University, specializing in system security, applied cryptography, and blockchain technology. She earned her Ph.D. in Computer Science and Engineering from the University of Connecticut. Dr. Maleki's research addresses critical security challenges, including IoT security, secure data retrieval in encrypted databases, and privacy-preserving data access in cloud environments. Her work leverages the Universally Composable (UC) security framework to analyze complex systems and employs multi-dimensional searchable encryption to protect massive scientific datasets. With over $1 million in NSF funding, her research advances scalable, efficient cryptographic solutions that meet the security needs of modern data-driven applications.

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