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C++代写 | C++ String Cleanup

C++代写 | C++ String Cleanup


Sequences are often analyzed in applications including location-based service provision, product recommendation, and DNA sequence analysis but this may lead to privacy breaches.

The focus of all 5 sub-topics below is the development of algorithms and their experimental evaluation. Strong knowledge of data structures and algorithms is needed, as well as strong programming skills (preferably in C++).

  1. The task is to protect the presence or the absence of all q-grams (substrings of q letters) in a given sequence by applying differential privacy: (see Sections 1,2, and 3).
  2. The task is to identify how to replace #s introduced by the TFS algorithm in ttps:// while minimizing tau-ghosts. See
  3. The task is to try to “break” the output of MCSR in by guessing where the #s might have been added and/or likely letters that #s in X corresponded to. See Section 6.3 of
  4. Consider a set of sequences X={X1,…,Xn} and a set of subsequences S that model confidential information. The Minimum Utility Loss Generalization problem seeks to transform X with minimum utility loss so that a notion of privacy, based on mutual information, is satisfied. The problem has been introduced in, where also heuristics have been proposed. The task is to implement, evaluate, and potentially adapt the heuristics to different problems.
  5. The top-k selection problem requires selecting the “best” k elements of a given set of elements U according to a quality function. Assume that each element of U is contributed by a different individual. Differential privacy can guarantee that the output k elements will not differ significantly (in a probabilistic sense) based on the input of any individual. The task is to implement and evaluate mechanisms that solve the top-k selection problem under differential privacy.

See *** Discussion on topics