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Tamer Mour (Bocconi University) – Computing on Encrypted Data; Extremely Fast and Simple
ינואר 5 @ 11:00 am - 12:00 pm
Title: Computing on Encrypted Data; Extremely Fast and Simple
Abstract:
Consider the following setting for computing on private data. A client uploads an encryption of a big input X to an untrusted server, and then wishes to make an unbounded number of queries f(X) while hiding f and X from the server and using only its secret key. How efficiently can this be done?
I will present a new framework for achieving the above for two useful special cases:
1- Data access (secret-key private information retrieval; sk-PIR), where X is a database and f(X)=X[i] for a secret index i.
2- Linear computations (encrypted matrix-vector product; EMVP), where X is a matrix and f(X)=Xv for a secret vector v.
Our framework yields extremely practical solutions, often approaching the concrete efficiency of cleartext computation across relevant metrics (down to a x1.25 overhead). This is enabled by a non-traditional approach to cryptographic design that prioritizes real-world considerations and from which new sources of hardness emerge.
In addition, we obtain new feasibility results for sk-PIR and EMVP based on the standard Learning Parity with Noise assumption (LPN), in a parameter regime not known to imply public-key encryption.
Consider the following setting for computing on private data. A client uploads an encryption of a big input X to an untrusted server, and then wishes to make an unbounded number of queries f(X) while hiding f and X from the server and using only its secret key. How efficiently can this be done?
I will present a new framework for achieving the above for two useful special cases:
1- Data access (secret-key private information retrieval; sk-PIR), where X is a database and f(X)=X[i] for a secret index i.
2- Linear computations (encrypted matrix-vector product; EMVP), where X is a matrix and f(X)=Xv for a secret vector v.
Our framework yields extremely practical solutions, often approaching the concrete efficiency of cleartext computation across relevant metrics (down to a x1.25 overhead). This is enabled by a non-traditional approach to cryptographic design that prioritizes real-world considerations and from which new sources of hardness emerge.
In addition, we obtain new feasibility results for sk-PIR and EMVP based on the standard Learning Parity with Noise assumption (LPN), in a parameter regime not known to imply public-key encryption.
Based on joint works with Fabrice Benhamouda, Caicai Chen, Yuval Ishai, Shai Halevi, Hugo Krawczyk, Tal Rabin and Alon Rosen.
