Waldo: A Private Time-Series Database from Function Secret Sharing

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About this Session

Applications today rely on cloud databases for storing and querying time-series data. While outsourcing storage is convenient, this data is often sensitive, making data breaches a serious concern. 

Prior systems such as TimeCrypt and Zeph have limited functionality and security: 

  • These systems can only filter on time
  • They reveal the queried time interval to the server.

While oblivious RAM (ORAM) and generic multiparty computation (MPC) are natural choices for eliminating leakage from prior work, but both are prohibitively expensive in our setting due to the number of roundtrips and bandwidth overhead, respectively. 

Presenting Waldo, a time-series database with rich functionality and strong security guarantees. Waldo supports multi-predicate filtering, protects data contents as well as query filter values and search access patterns, and provides malicious security in the 3rd party honest-majority setting. With 32-core machines, Waldo runs a query with 8 range predicates over records in 3.03s, compared to 12.88s for an MPC baseline and 16.56s for an ORAM baseline.  

Come learn more from Soroco’s guest speaker Emma Dauterman, a UC Berkeley Ph.D. student whose work appeared at IEEE S&P (Oakland) ’22.

Our Speaker

Emma Dauterman is a fourth-year Ph.D. student at UC Berkeley where she is advised by Raluca AdaPopa and Ion Stoica. She is broadly interested in building secure systems using cryptography. Her work is funded by a Microsoft Ada Lovelace Research fellowship and a NSF GFRP fellowship. View her profile: 

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