Digital　Microfluidic　Biochip　(DMFB)　-based　molecular　diagnostic　techniques　have　recently　become　hot　topics.　Compared　with　traditional　molecular　diagnostic　techniques,　digital　microfluidic　biochips　have　advantages　in　precise　control　of　discrete　droplets　and　execution　of　biochemical　protocols.　However,　as　components　of　networked　cyber-physical　systems,　potential　privacy　and　security　issues　of　biochips　are　increasingly　prominent,　for　instance,　eavesdropping　attacks　on　communication　channels,　tampering　attacks　on　biochemical　protocols,　and　copyright　attacks　on　physical　structure　protection.　Differential　Privacy　(DP),　a　de　facto　standard　for　achieving　privacy,　is　trying　to　incorporate　DMFB　applications　to　protect　user　privacy.　However,　as　the　intersection　of　privacy-preserving　technology,　DMFB　applications,　and　DMFB　security,　comprehensive　research　on　this　area　is　relatively　rare.　Investigating　and　analyzing　the　implementation　mechanisms　and　threat　models　of　biochip　applications,　including　biochemical　protocols,　cyber-physical　systems,　and　Enhanced　privacy　protection　DMFB’s　user　data　security　platform,　this　paper　proposes　the　application　scenarios　and　protection　schemes　of　differential　privacy　techniques　on　DMFB　user　data　platform.　Firstly,　the　application　scenarios　and　protection　schemes　of　Laplace　mechanism,　Gaussian　mechanism,　and　random　response　mechanism　were　described　on　the　DMFB　user　data　platform.　Secondly,　parameter　security　publishing　algorithms　were　proposed　based　on　three　strategies:　user　level　sensitivity,　routing　weight　set,　and　routing　intersection　parameter　set.　Finally,　tamper　proof　probability　was　created　as　a　security　indicator,　while　confidence　scores,　calibration,　and　cumulative　error　rate　were　established　to　measure　data　availability.　The　simulation　experiment　results　show　that　the　overall　privacy　security　of　the　scheme　can　reach　100%,　and　the　average　data　availability　can　reach　93.3%.　The　algorithm　performance　test　shows　that　the　optimal　privacy　budget　range　of　the　scheme　is　0.4.　In　addition,　compared　with　similar　algorithms,　the　proposed　scheme　improves　privacy　security　by　12.09%　on　average,　and　data　availability　by　7.02%.　Therefore,　this　scheme　can　be　a　secure　and　effective　user　data　platform　for　DMFB　to　execute　biochemical　protocols.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.