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With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. <i>k</i>-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory <i>k</i>-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are <i>n</i> similar point sets, each consisting of <i>m</i> points. The size of the space is then <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>m</mi><mi>n</mi></msup></semantics></math></inline-formula>. Furthermore, to choose suitable <i>k</i>− 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a <i>k</i>-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mn>1</mn></mrow></semantics></math></inline-formula> dummy trajectories that are indistinguishable from real trajectories, effectively achieving <i>k</i>-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.