Nowadays, the standard privacy-preserving mechanism is Differential Privacy. It aims to hide the presence or the absence of a data point in the final result by adding noise to the original query, making the two outcomes (one with and one without a single data point) statistically indistinguishable (up to the privacy- parameter). For example, the average salary of BME last year's graduates are published with added noise, so even if an adversary knows all alums' salaries except its target, it cannot deduce that with certainty.  
 
Besides the size of the added noise, privacy protection can further be increased by so-called amplification techniques, such as sampling from the data (instead of utilizing all). For instance, only half of the alums are considered for this statistic. The student's task is to learn and experiment with these amplification techniques and to find the optimal setting (amplification mechanisms and its parameters) to obtain a desirable trade-off between the provided privacy protection and the obtained accuracy. 

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