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Purpose In this work, an efficient architecture for memory built in self-test (MBIST) that incorporates a modified March Y algorithm using concurrent technique and a modified linear feedback shift register (LFSR)–based address generator is proposed. Design/methodology/approach Built in self-test (BIST) is emerging as the essential ingredient of the system on chip. In the ongoing high speed, high tech sophistication technology of the very large-scale integrated circuits, testing of these memories is a very tedious and challenging job, since the area overhead, the testing time and the cost of the test play an important role. Findings With the efficient service of the adapted architecture, switching activity is considerably cut down. As the switching activity is in direct proportion to the power consumed scaling down, the switching process of the address generator inevitably leads to the reduction in power consumption of the MBIST. Originality/value To improve the yield and fault tolerance of on-chip memories without degradation on its performance self-repair mechanisms can be implemented on chip.

Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the Internet. Nowadays, access control is one of the most critical problems with cloud computing. Ciphertext-Policy Attribute Based Encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. This paper proposes an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, it hides the whole attribute (rather than only its values) in the access policies. To assist data decryption, it designs an algorithm called Attribute Bloom Filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. The paper also deals with offline attribute guessing attack. Security analysis and performance evaluation show that this scheme can preserve the privacy from any LSSS access policy without employing much overhead.