Preservation of Private Information using Secure Multi-Party Computation

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Unconditionally 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 کامل

Heterogeneous Secure Multi-Party Computation

The increased processing power and storage capacity of inhome and mobile computing devices has motivated their inclusion in distributed and cloud computing systems. The resulting diverse environment creates a strong requirement for secure computations, which can be realised by Secure Multi-Party Computation (MPC). However, MPC most commonly assumes that parties performing the secure computation...

متن کامل

Efficient Secure Multi-party Computation

Since the introduction of secure multi-party computation, all proposed protocols that provide security against cheating players suffer from very high communication complexities. The most efficient unconditionally secure protocols among n players, tolerating cheating by up to t < n/3 of them, require communicating O(n6) field elements for each multiplication of two elements, even if only one pla...

متن کامل

Aggregating Private Sparse Learning Models Using Multi-Party Computation

We consider the problem of privately learning a sparse model across multiple sensitive datasets, and propose learning individual models locally and privately aggregating them using secure multi-party computation. In this paper, we report some preliminary experiments on distributed sparse linear discriminant analysis, showing both the feasibility and effectiveness of our approach on experiments ...

متن کامل

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 کامل

ذخیره 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: Indian Journal of Science and Technology

سال: 2016

ISSN: 0974-5645,0974-6846

DOI: 10.17485/ijst/2016/v9i14/74588