Recently,　evolutionary　computation　(EC)　has　experienced　significant　advancements　due　to　the　integration　of　machine　learning,　distributed　computing,　and　big　data　technologies.　These　developments　have　led　to　new　research　avenues　in　EC,　such　as　distributed　EC　and　surrogate-assisted　EC.　While　these　advancements　have　greatly　enhanced　the　performance　and　applicability　of　EC,　they　have　also　raised　concerns　regarding　privacy　leakages,　specifically　the　disclosure　of　optimal　results　and　surrogate　models.　Consequently,　the　combination　of　evolutionary　computation　and　privacy　protection　becomes　an　increasing　necessity.　However,　a　comprehensive　exploration　of　privacy　concerns　in　evolutionary　computation　is　currently　lacking,　particularly　in　terms　of　identifying　the　object,　motivation,　position,　and　method　of　privacy　protection.　To　address　this　gap,　this　paper　aims　to　discuss　three　typical　optimization　paradigms,　namely,　centralized　optimization,　distributed　optimization,　and　data-driven　optimization,　to　characterize　optimization　modes　of　evolutionary　computation　and　proposes　BOOM　(i.e.,　oBject,　mOtivation,　pOsition,　and　Method)　to　sort　out　privacy　concerns　related　to　evolutionary　computation.　In　particular,　the　centralized　optimization　paradigm　allows　clients　to　outsource　optimization　problems　to　a　centralized　server　and　obtain　optimization　solutions　from　the　server.　The　distributed　optimization　paradigm　exploits　the　storage　and　computational　power　of　distributed　devices　to　solve　optimization　problems.　On　the　other　hand,　the　data-driven　optimization　paradigm　utilizes　historical　data　to　address　optimization　problems　without　explicit　objective　functions.　Within　each　of　these　paradigms,　BOOM　is　used　to　characterize　the　object　and　motivation　of　privacy　protection.　Furthermore,　this　paper　discuss　the　potential　privacy-preserving　technologies　that　strike　a　balance　between　optimization　performance　and　privacy　guarantees.　Finally,　this　paper　outlines　several　new　research　directions　for　privacy-preserving　evolutionary　computation.