The Dawn of Fully Automated Contract Drafting: Machine Learning Breathes New Life Into a Decades-Old Promise

Technological advances within contract drafting software have seemingly plateaued. Despite the decades-long hopes and promises of many commentators, critics doubt this technology will ever fully automate the drafting process. But, while there has been a lack of innovation in contract drafting software, technological advances have continued to improve contract review and analysis programs. “Machine learning,” the leading innovative force in these areas, has proven incredibly efficient, performing in mere minutes tasks that would otherwise take a team of lawyers tens of hours. Some contract drafting programs have already experimented with machine learning capabilities, and this technology may pave the way for the full automation of contract drafting. Although intellectual property, data access, and ethical obstacles may delay complete integration of machine learning into contract drafting, full automation is likely still viable

Adaptively Secure Computationally Efficient Searchable Symmetric Encryption

Searchable encryption is a technique that allows a client to store documents on a server in encrypted form. Stored documents can be retrieved selectively while revealing as little information as\ud possible to the server. In the symmetric searchable encryption domain, the storage and the retrieval are performed by the same client. Most conventional searchable encryption schemes suffer\ud from two disadvantages.\ud First, searching the stored documents takes time linear in the size of the database, and/or uses heavy arithmetic operations.\ud Secondly, the existing schemes do not consider adaptive attackers;\ud a search-query will reveal information even about documents stored\ud in the future. If they do consider this, it is at a significant\ud cost to updates.\ud In this paper we propose a novel symmetric searchable encryption\ud scheme that offers searching at constant time in the number of\ud unique keywords stored on the server. We present two variants of\ud the basic scheme which differ in the efficiency of search and\ud update. We show how each scheme could be used in a personal health\ud record system