This paper presents an efficient immunization strategy for computer networks and populations with scale-free degree distributions. The strategy, called acquaintance immunization, involves immunizing random acquaintances of randomly selected nodes. Unlike targeted immunization, which requires global knowledge of the network, this strategy only needs local information. The authors show that this approach significantly reduces the immunization threshold needed to stop an epidemic, making it more efficient. The study uses the SIR (susceptible-infected-removed) epidemiological model to analyze the effectiveness of the strategy. It is shown that acquaintance immunization reduces the critical immunization fraction, $ f_c $, required to stop an epidemic. For scale-free networks, this threshold is dramatically reduced, even for cases where random immunization would require almost 100% immunization. The strategy is effective for any broad-scale distributed network, including scale-free and bimodal distributions. It is also applicable to assortatively mixed networks, where high-degree nodes tend to connect to other high-degree nodes. The strategy is also effective in networks with geographical effects, where nodes tend to connect to geographically adjacent ones. The authors also consider the SIR model with finite infection probabilities, showing that the strategy still provides significant improvements in immunization thresholds. The strategy is less sensitive to manipulation, as it relies on acquaintance reports rather than self-estimates of contacts. This makes it more practical for real-world applications. In conclusion, the acquaintance immunization strategy is a novel and efficient method for immunizing computer networks and populations. It requires no global knowledge of the network and can be applied to a wide range of network structures. The strategy is particularly effective for scale-free networks and can significantly reduce the immunization threshold needed to stop an epidemic.This paper presents an efficient immunization strategy for computer networks and populations with scale-free degree distributions. The strategy, called acquaintance immunization, involves immunizing random acquaintances of randomly selected nodes. Unlike targeted immunization, which requires global knowledge of the network, this strategy only needs local information. The authors show that this approach significantly reduces the immunization threshold needed to stop an epidemic, making it more efficient. The study uses the SIR (susceptible-infected-removed) epidemiological model to analyze the effectiveness of the strategy. It is shown that acquaintance immunization reduces the critical immunization fraction, $ f_c $, required to stop an epidemic. For scale-free networks, this threshold is dramatically reduced, even for cases where random immunization would require almost 100% immunization. The strategy is effective for any broad-scale distributed network, including scale-free and bimodal distributions. It is also applicable to assortatively mixed networks, where high-degree nodes tend to connect to other high-degree nodes. The strategy is also effective in networks with geographical effects, where nodes tend to connect to geographically adjacent ones. The authors also consider the SIR model with finite infection probabilities, showing that the strategy still provides significant improvements in immunization thresholds. The strategy is less sensitive to manipulation, as it relies on acquaintance reports rather than self-estimates of contacts. This makes it more practical for real-world applications. In conclusion, the acquaintance immunization strategy is a novel and efficient method for immunizing computer networks and populations. It requires no global knowledge of the network and can be applied to a wide range of network structures. The strategy is particularly effective for scale-free networks and can significantly reduce the immunization threshold needed to stop an epidemic.