隨著大數據與人工智慧技術的發展，各企業或政府單位紛紛收集了許多的資料。這些資料如果能夠共享，則可以減少較不具經濟資源的個人或組織收集資料的困難，乃至於可以讓一個組織因為取得使用者各方面的資料而更了解該使用者。然而在資料公開透明化的結果下，恐將民眾的個人隱私公布於大眾之下，或通過各種分享資料的結合可窺探到一些機密等問題，為保障分享的資料不被有心人士利用。研究學者們針對資料的隱私問題，一直不斷的精進研究各種可以分享並保護資料隱私的方法，並隨著跨組織或公私合作的資料交換需求越多，資料的隱私性訴求也越大，重要性也越重。
本研究統整2015年至2020年內相關跨組織聯合資料分享之資料隱私保護方法，並歸納出多種隱私保護方法，主要以匿名化、差分隱私、加密、聯邦學習及秘密共享，並針對這些方法進行分析。主要分析各種方法可能尚存的風險，並判斷隱私保護的程度，其較多用於何種形式，能否保留資料的原始性。通過本研究的分析去看見，當前最適用的隱私方法及各種隱私方法的進步空間與未來的發展趨勢。

Currently, organizations usually collect user data to understand their users via big data or AI technologies. If an organization can obtain a user's data from other organizations, the organization can more understand the user. Therefore, more and more organizations are urged to share their data or to participate a alliance to share data to alliance member,. However data transparency comes with the consequences of the fear of personal details being exposed publicly, or private files being compromised .To ensure shared information isn’t being capitalized by the wrong people, experts have been working on ways to improve and protect information privacy, as demand for information exchange increases, so does the importance of privacy needs.
This research has gathered and analyzed data on cross organization data sharing from the year 2015 to 2020, concluding multiple ways for security, prioritizing in anonymous, differential privacy, encryption, federated learning and secret sharing. The analysis is mainly focused on potential risks that maybe left out from the methods above, assessing the degree of privacy protection from each one, to see what methods are being practiced and if they can retain originality from the data. Through this research we can find the most optimal information security methods, in additionally, finding room for improvement and forecasting future trends.

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