Demo Data from VLDB 2026 / reviewers in the wild / expert
Xionghong Jiang
dblp:243/2889
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Cryptographic protocols and secure computation · 50% Privacy and data protection · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection › privacy-preserving computation
access pattern hiding |
0.4 | 1 | 2019 | Strongly Secure and Efficient Range Queries in Cloud Databases under Multiple Keys · INFOCOM 2019 |
Cryptographic protocols and secure computation
secure query processing |
0.4 | 1 | 2019 | Strongly Secure and Efficient Range Queries in Cloud Databases under Multiple Keys · INFOCOM 2019 |
Methods — techniques the papers use, named apart from their topics
two-cloud architecture · 0.4distributed two-trapdoor public key cryptosystem · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Strongly Secure and Efficient Range Queries in Cloud Databases under Multiple KeysabstractCloud database provides an advantageous platform for outsourcing of database service. To protect data confidentiality from an untrusted cloud, the original database is often encrypted and then uploaded to the cloud. However, in order to support functional queries, existing secure databases require users to encrypt their data under the same public/symmetric key, which restricts the usage scenarios since users do not really trust each other in practice. Imagine a scenario where a user uploaded his/her own encrypted data to the cloud database and another user wants to execute private range queries on this data. This scenario occurs in many cases of collaborative statistical analysis where the data provider and analyst are different entities. Then either the data provider must reveal its encryption key or the analyst must reveal the private queries. In this paper, we overcome this restriction for secure range queries by enabling query executions on the multi-key encryption data. We propose a secure cloud database supporting range queries under multiple keys, in which all users could preserve the confidentiality of their own different keys, and do not have to share them with each other. At a higher level, our system is constructed on a two-cloud architecture and a novel distributed two-trapdoor public key cryptosystem. We prove that the proposed scheme achieves the goal of a secure query without leaking data privacy, query privacy, and data access patterns. Finally, we use extensive experiments over a real-world dataset on a commercial cloud platform to verify the efficacy of our proposed scheme. Ke Cheng 0001, Yulong Shen 0001, Yongzhi Wang 0001, Liangmin Wang 0001, Jianfeng Ma 0001, Xionghong Jiang, Cuicui Su |
INFOCOM | 6 |