Biblio
An efficient secure two-party computation protocol of matrix multiplication allows privacy-preserving cloud-aid machine learning services such as face recognition and traffic-aware navigation. We use homomorphic encryption to construct a secure matrix multiplication protocol with a small communication overhead and computation overhead on the client's side, which works particularly well when a large number of clients access to the server simultaneously. The fastest secure matrix multiplication protocols have been constructed using tools such as oblivious transfer, but a potential limitation of these methods is the needs of using a wide network bandwidth between the client and the server, e.g., 10\textasciitildeGbps. This is of particular concern when thousands of clients interact with the server concurrently. Under this setting, the performance oblivious transfer-based methods will decrease significantly, since the server can only allocate a small ratio of its outgoing bandwidth for each client. With three proposed optimizations, our matrix multiplication protocol can run very fast even under the high concurrent setting. Our benchmarks show that it takes an Amazon instance (i.e., 72 CPUs and 25 Gbps outgoing bandwidth) less than 50 seconds to complete 1000 concurrent secure matrix multiplications with \$128\textbackslashtimes 128\$ entries. In addition, our method reduces more than \$74% - 97%\$ of the precomputation time of two privacy-preserving machine learning frameworks, SecureML (S&P'17) and MiniONN (CCS'17).
This paper presents a method to extract important byte sequences in malware samples by application of convolutional neural network (CNN) to images converted from binary data. This method, by combining a technique called the attention mechanism into CNN, enables calculation of an "attention map," which shows regions having higher importance for classification in the image. The extracted region with higher importance can provide useful information for human analysts who investigate the functionalities of unknown malware samples. Results of our evaluation experiment using malware dataset show that the proposed method provides higher classification accuracy than a conventional method. Furthermore, analysis of malware samples based on the calculated attention map confirmed that the extracted sequences provide useful information for manual analysis.
Let us consider a scenario that a data holder (e.g., a hospital) encrypts a data (e.g., a medical record) which relates a keyword (e.g., a disease name), and sends its ciphertext to a server. We here suppose not only the data but also the keyword should be kept private. A receiver sends a query to the server (e.g., average of body weights of cancer patients). Then, the server performs the homomorphic operation to the ciphertexts of the corresponding medical records, and returns the resultant ciphertext. In this scenario, the server should NOT be allowed to perform the homomorphic operation against ciphertexts associated with different keywords. If such a mis-operation happens, then medical records of different diseases are unexpectedly mixed. However, in the conventional homomorphic encryption, there is no way to prevent such an unexpected homomorphic operation, and this fact may become visible after decrypting a ciphertext, or as the most serious case it might be never detected. To circumvent this problem, in this paper, we propose mis-operation resistant homomorphic encryption, where even if one performs the homomorphic operations against ciphertexts associated with keywords ω' and ω, where ω -ω', the evaluation algorithm detects this fact. Moreover, even if one (intentionally or accidentally) performs the homomorphic operations against such ciphertexts, a ciphertext associated with a random keyword is generated, and the decryption algorithm rejects it. So, the receiver can recognize such a mis-operation happens in the evaluation phase. In addition to mis-operation resistance, we additionally adopt secure search functionality for keywords since it is desirable when one would like to delegate homomorphic operations to a third party. So, we call the proposed primitive mis-operation resistant searchable homomorphic encryption (MR-SHE). We also give our implementation result of inner products of encrypted vectors. In the case when both vectors are encrypted, the running time of the receiver is millisecond order for relatively small-dimensional (e.g., 26) vectors. In the case when one vector is encrypted, the running time of the receiver is approximately 5 msec even for relatively high-dimensional (e.g., 213) vectors.