Name
Abstract | ||
|---|---|---|
We consider privacy-preserving computation of big data using trusted computing primitives with limited private memory. Simply ensuring that the data remains encrypted outside the trusted computing environment is insufficient to preserve data privacy, because data movement observed during computation could leak information. Designing algorithms that thwart such leakage is challenging. Many known privacypreserving algorithms are complex and induce large trusted code bases that are unwieldy to vet and verify. In this paper, we make a key observation that many basic algorithms (e.g. sorting) can be made privacypreserving by adding a step that securely scrambles the data before feeding it to the original algorithms. We call this approach Scramblethen-Compute (StC), and give a sufficient condition whereby existing external memory algorithms can be made privacy-preserving via StC. This approach facilitates code-reuse, and its simplicity contributes to a smaller trusted code base. It is also general, allowing algorithm designers to leverage on the rich set of known algorithms for better performance. Our experiments show that StC could offer up to 4.1× speedups over known, application-specific alternatives. |
Year | Venue | Field |
|---|---|---|
2017 | PoPETs | Trusted Computing,Computer science,Computer security,Encryption,Sorting,Out-of-core algorithm,Direct Anonymous Attestation,Secure two-party computation,Information privacy,Big data,Distributed computing |
DocType | Volume | Issue |
Journal | 2017 | 3 |
Citations | PageRank | References |
2 | 0.37 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung Dang | 1 | 2 | 4.76 |
| Tien Tuan Anh Dinh | 2 | 212 | 19.13 |
| Ee-chien Chang | 3 | 835 | 75.36 |
| Beng Chin Ooi | 4 | 7873 | 1076.70 |