Secure Computation for Big Data

نویسنده

  • Tal Malkin
چکیده

Secure computation has been a powerful and important research area in cryptography since the first breakthrough results in the 1980s. For many years this area was purely theoretical, as the feasibility results have not been considered even close to practical. Recently, it appears to have turned a corner, with several research efforts showing that secure computation for large classes of functions, and even generic secure computation, has the potential to become truly practical. This shift is brought on by algorithmic advancements and new cryptographic tools, alongside advancements in CPU speed, parallelism, and storage capabilities; it is further motivated by the explosion of new potential application domains for secure computation. A compelling motivation for making secure computation practical is provided by the burgeoning field of Big Data, representing the deluge of data being generated, collected, and stored all around us. Protocols for secure computation on big data can provide critical value for many business, medical, legal, and personal applications. However, conventional approaches to secure computation are inherently insufficient in this setting, where even linear computation can be too prohibitive. In this talk I discuss challenges and solutions related to secure computation for big data, following two thrusts: – Overcoming inherent theoretical bounds of (in)efficiency; and – Satisfying immediate practical needs in a theoretically sound way. Both goals require the development of new models of secure computation, allowing for theoretically and practically meaningful relaxations of the standard model. In particular, I discuss a few works I have participated in over the last decade, which address the challenge of achieving efficient secure computation for massive data. I also share some experiences from the last few years working on secure search over massive data sets. This research has externally imposed practical constraints, such as strict performance requirements. I focus on my perspective as a theoretical cryptographer and discuss some open cryptographic challenges in this emerging domain.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Secure Multiparty RAM Computation in Constant Rounds

Secure computation of a random access machine (RAM) program typically entails that it be first converted into a circuit. This conversion is unimaginable in the context of big-data applications where the size of the circuit can be exponential in the running time of the original RAM program. Realizing these constructions, without relinquishing the efficiency of RAM programs, often poses considera...

متن کامل

Efficient Privacy-Preserving Big Data Processing through Proxy-Assisted ORAM

We present a novel mechanism that allows a client to securely outsource his private data to the cloud while at the same time to delegate to a third party the right to run certain algorithms on his data. The mechanism is privacy-preserving, meaning that the third party only learns the result of his algorithm on the client’s data, while at the same time the access pattern on the client’s data is ...

متن کامل

Students and Taxes: a Privacy-Preserving Study Using Secure Computation

We describe the use of secure multi-party computation for performing a large-scale privacypreserving statistical study on real government data. In 2015, statisticians from the Estonian Center of Applied Research (CentAR) conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database o...

متن کامل

Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation

We describe the use of secure multi-party computation for performing a large-scale privacy-preserving statistical study on real government data. In 2015, statisticians in Estonia conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database of individual tax payments from the Estonia...

متن کامل

PrivPy: Enabling Scalable and General Privacy-Preserving Computation

We introduce PrivPy, a practical privacy-preserving collaborative computation framework. PrivPy provides an easy-to-use and highly compatible Python programming front-end which supports high-level array operations and different secure computation engines to allow for security assumptions and performance trade-offs. We also design and implement a new secret-sharing-based computation engine with ...

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

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013