The authors propose an efficient immunization strategy called *acquaintance immunization* for computer networks and populations with broad, scale-free degree distributions. This strategy involves immunizing random acquaintances of randomly selected nodes, requiring no knowledge of node degrees or global network information. The critical threshold for complete immunization is studied analytically, and the strategy is evaluated using the SIR (susceptible-infected-removed) epidemiological model. The results show that the immunization threshold is significantly reduced with the acquaintance immunization strategy compared to random immunization. The strategy is effective for various network distributions, including scale-free and bimodal distributions, and is particularly useful in assortatively mixed networks where high-degree nodes tend to connect to other high-degree nodes. The authors also discuss the strategy's effectiveness in the context of the SIR model and its potential applications in other networks, such as ecological and terrorist networks.The authors propose an efficient immunization strategy called *acquaintance immunization* for computer networks and populations with broad, scale-free degree distributions. This strategy involves immunizing random acquaintances of randomly selected nodes, requiring no knowledge of node degrees or global network information. The critical threshold for complete immunization is studied analytically, and the strategy is evaluated using the SIR (susceptible-infected-removed) epidemiological model. The results show that the immunization threshold is significantly reduced with the acquaintance immunization strategy compared to random immunization. The strategy is effective for various network distributions, including scale-free and bimodal distributions, and is particularly useful in assortatively mixed networks where high-degree nodes tend to connect to other high-degree nodes. The authors also discuss the strategy's effectiveness in the context of the SIR model and its potential applications in other networks, such as ecological and terrorist networks.