Cryptography and Data Security

We consider the problem of efficiently proving the integrity of data stored at untrusted servers. In the provable data possession (PDP) model, the client preprocesses the data and then sends it to an untrusted server for storage, while keeping a small amount of meta-data. The client later asks the server to prove that…

Montgomery multiplication methods constitute the core of modular exponentiation, the most popular operation for encrypting and signing digital data in public-key cryptography. In this article, we study the operations involved in computing the Montgomery product, describe several high-speed, space-efficient algorithms…

• ÇETİN KAYA KOÇ

As storage-outsourcing services and resource-sharing networks have become popular, the problem of efficiently proving the integrity of data stored at untrusted servers has received increased attention. In the Provable Data Possession (PDP) model, the client preprocesses the data and then sends them to an untrusted ser…

• ALPTEKİN KÜPÇÜ

This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical values, such as click count in Web access logs. However, in f…

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• ÇETİN KAYA KOÇ

There is no doubt that the revolutionary concept of the blockchain, which is the underlying technology behind the famous cryptocurrency Bitcoin and its successors, is triggering the start of a new era in the Internet and the online services. While most people focus only at cryptocurrencies; in fact, many administrativ…

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates thr…

• BETÜL YÜRDEM

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates thr…

• MEHMET KEMAL GÜLLÜ

This paper presents a scalable architecture for the computation of modular multiplication, based on the Montgomery multiplication (MM) algorithm. A word-based version of MM is presented and used to explain the main concepts in the hardware design. The proposed multiplier is able to work with any precision of the input…

• ÇETİN KAYA KOÇ

We present a new low-complexity bit-parallel canonical basis multiplier for the field GF(2m) generated by an all-one-polynomial. The proposed canonical basis multiplier requires m/sup 2/-1 XOR gates and m/sup 2/ AND gates. We also extend this canonical basis multiplier to obtain a new bit-parallel normal basis multipl…

• ÇETİN KAYA KOÇ