.png){kind=icon}
返回
Private Multi-Group Aggregation
.png){kind=icon}
OA
AI
DOI：10.1109/JSAC.2022.3142357.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We study the differentially private multi-group aggregation (PMGA) problem. This setting involves a single server and n users. Each user belongs to one of k distinct groups and holds a discrete value. The goal is to design schemes that allow the server to find the aggregate (sum) of the values in each group (with high accuracy) under communication and local differential privacy constraints. The privacy constraint guarantees that the user's group remains private. This is motivated by applications where a user's group can reveal sensitive information, such as his religious and political beliefs, health condition, or race. We propose a novel scheme, dubbed Query and Aggregate (Q&A) for PMGA. The novelty of Q&A is that it is an interactive aggregation scheme. In Q&A, each user is assigned a random query matrix, to which he sends the server an answer based on his group and value. We characterize the Q&A scheme's performance in terms of accuracy (MSE), privacy, and communication. We compare Q&A to the Randomized Group (RG) scheme, which is non-interactive and adapts existing randomized response schemes to the PMGA setting. We observe that typically Q&A outperforms RG, in terms of privacy vs. utility, in the high privacy regime.
Keyword:
Servers
Privacy
Costs
Aggregates
Random variables
Estimation
Differential privacy
Differential privacy
data privacy
estimation
期刊
IF:
17.2
论文数:
6.4K
被引数:
3.1W
机构
引用论文
.png){kind=icon}
分享收藏Modeling and Simulation of Main Control Valve Mechanism of LNG Ship Steam TurbineLNG船舶汽轮机主控阀机构建模与仿真OAAI
OAAI分享收藏The Ada protein acts as both a positive and a negative modulator of Escherichia coli's response to methylating agents.Ada蛋白充当大肠杆菌对甲基化试剂的反应的阳性和阴性调节剂。
OAAI
OAAI分享收藏
查看更多内容