Differential privacy addresses the limitations of traditional statistical analysis by ensuring that the results are not overly influenced by any individual's data. It responds to the fundamental law of information reconstruction, which states that overly accurate estimates of too many statistics can destroy privacy. By adding a controlled amount of randomness to the data or to the results of queries, differential privacy prevents precise inferences about any individual, thus safeguarding privacy while allowing for meaningful aggregate analysis. This approach maintains the utility of statistics for broader analysis purposes while protecting individual confidentiality.