Data trading is a crucial component of the digital economy; however, it faces several challenges. The data capacity must exceed the market's demand due to the replicability of data, which results in excess capacity. Identical data may be sold multiple times or by multiple parties simultaneously, disrupting the data trading market. Data privacy concerns impede data trading and its usability. Unfortunately, how to tackle the above challenges remains an open problem. To this end, this paper aims to discuss two types of data trading: on-exchange trading and off-exchange trading, and seeks to answer two key questions:
- Can trading data be prevented from being resold, given that it can be copied?
- If the answer to the first question is yes, can privacy-preserving data trading be achieved through secure computation? Subsequently, this paper presents privacy-preserving on-exchange data trading using secure outsourced computation and privacy-preserving off-exchange data trading through secure two-party computation. Finally, it outlines the research challenges associated with privacy-preserving data trading.