Privacy-Aware MapReduce Based Multi-Party Secure Skyline Computation
نویسندگان
چکیده
منابع مشابه
Privacy Preserving PageRank Algorithm By Using Secure Multi-Party Computation
In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively using homomorphic encryption based on Paillier scheme. In the proposed PageRank computation, a user encrypt its own graph data using asymmetric encryption method, sends the data set into different parties in a privacy-...
متن کاملPrivacy-Preserving Classification and Clustering Using Secure Multi-Party Computation
Nowadays, data mining and machine learning techniques are widely used in electronic applications in different areas such as e-government, e-health, e-business, and so on. One major and very crucial issue in these type of systems, which are normally distributed among two or more parties and are dealing with sensitive data, is preserving the privacy of individual’s sensitive information. Each par...
متن کاملQuorum-Based Secure Multi-party Computation
This paper describes efficient protocols for multi-party computations that are information-theoretically secure against passive attacks. The results presented here apply to access structures based on quorum systems, which are collections of sets enjoying a naturallymotivated self-intersection property. Quorum-based access structures include threshold systems but are far richer and more general,...
متن کاملSecure Multi-party Differential Privacy
We study the problem of interactive function computation by multiple parties, each possessing a bit, in a differential privacy setting (i.e., there remains an uncertainty in any party’s bit even when given the transcript of interactions and all the other parties’ bits). Each party wants to compute a function, which could differ from party to party, and there could be a central observer interest...
متن کاملUnconditionally Secure Multi-Party Computation
The most general type of multi-party computation involves n participants. Participant i supplies private data xi and obtains an output function fi(x1, . . . , xn). The computation is said to be unconditionally secure if each participant can verify, with probability arbitrarily close to one, that every other participant obtains arbitrarily little information beyond their agreed output fi. We giv...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information
سال: 2019
ISSN: 2078-2489
DOI: 10.3390/info10060207