Splinter: Practical Private Queries on Public Data
Many online services let users query public datasets such as maps, flight prices, or restaurant reviews. Unfortunately, the queries to these services reveal highly sensitive information that can compromise users’ privacy. This paper presents Splinter, a system that protects users’ queries on public...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/1721.1/137564 |
| _version_ | 1826196288794787840 |
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| author | Wang, Frank Yun, Catherine Goldwasser, Shafi Vaikuntananthan, Vinod Zaharia, Matei |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Wang, Frank Yun, Catherine Goldwasser, Shafi Vaikuntananthan, Vinod Zaharia, Matei |
| author_sort | Wang, Frank |
| collection | MIT |
| description | Many online services let users query public datasets such as maps, flight prices, or restaurant reviews. Unfortunately, the queries to these services reveal highly sensitive information that can compromise users’ privacy. This paper presents Splinter, a system that protects users’ queries on public data and scales to realistic applications. A user splits her query into multiple parts and sends each part to a different provider that holds a copy of the data. As long as any one of the providers is honest and does not collude with the others, the providers cannot determine the query. Splinter uses and extends a new cryptographic primitive called Function Secret Sharing (FSS) that makes it up to an order of magnitude more efficient than prior systems based on Private Information Retrieval and garbled circuits. We develop protocols extending FSS to new types of queries, such as MAX and TOPK queries. We also provide an optimized implementation of FSS using AES-NI instructions and multicores. Splinter achieves end-to-end latencies below 1.6 seconds for realistic workloads including a Yelp clone, flight search, and map routing. |
| first_indexed | 2024-09-23T10:24:14Z |
| format | Article |
| id | mit-1721.1/137564 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:24:14Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | mit-1721.1/1375642022-09-30T20:54:54Z Splinter: Practical Private Queries on Public Data Wang, Frank Yun, Catherine Goldwasser, Shafi Vaikuntananthan, Vinod Zaharia, Matei Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Many online services let users query public datasets such as maps, flight prices, or restaurant reviews. Unfortunately, the queries to these services reveal highly sensitive information that can compromise users’ privacy. This paper presents Splinter, a system that protects users’ queries on public data and scales to realistic applications. A user splits her query into multiple parts and sends each part to a different provider that holds a copy of the data. As long as any one of the providers is honest and does not collude with the others, the providers cannot determine the query. Splinter uses and extends a new cryptographic primitive called Function Secret Sharing (FSS) that makes it up to an order of magnitude more efficient than prior systems based on Private Information Retrieval and garbled circuits. We develop protocols extending FSS to new types of queries, such as MAX and TOPK queries. We also provide an optimized implementation of FSS using AES-NI instructions and multicores. Splinter achieves end-to-end latencies below 1.6 seconds for realistic workloads including a Yelp clone, flight search, and map routing. 2021-11-05T18:26:03Z 2021-11-05T18:26:03Z 2017 2019-05-29T16:03:52Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137564 Wang, Frank, Yun, Catherine, Goldwasser, Shafi, Vaikuntananthan, Vinod and Zaharia, Matei. 2017. "Splinter: Practical Private Queries on Public Data." en https://www.usenix.org/system/files/conference/nsdi17/nsdi17-wang-frank.pdf Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Other repository |
| spellingShingle | Wang, Frank Yun, Catherine Goldwasser, Shafi Vaikuntananthan, Vinod Zaharia, Matei Splinter: Practical Private Queries on Public Data |
| title | Splinter: Practical Private Queries on Public Data |
| title_full | Splinter: Practical Private Queries on Public Data |
| title_fullStr | Splinter: Practical Private Queries on Public Data |
| title_full_unstemmed | Splinter: Practical Private Queries on Public Data |
| title_short | Splinter: Practical Private Queries on Public Data |
| title_sort | splinter practical private queries on public data |
| url | https://hdl.handle.net/1721.1/137564 |
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